Package: a4 Version: 1.44.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats License: GPL-3 MD5sum: cc696d3373a9f258d293f2d966da11d5 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: RELEASE_3_15 git_last_commit: 5b0fc5a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4_1.44.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 82 Package: a4Base Version: 1.44.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 094c0a1c87b18ff8f16a3dbe4d06da64 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: RELEASE_3_15 git_last_commit: 9ae69e0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4Base_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Base_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Base_1.44.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 73 Package: a4Classif Version: 1.44.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 1f6e6aab041fff5059d0449e3ed3da9e NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: RELEASE_3_15 git_last_commit: df0fce7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4Classif_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Classif_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Classif_1.44.0.tgz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 32 Package: a4Core Version: 1.44.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 9413ac5f3064767be54d0075bb0fd2b4 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: RELEASE_3_15 git_last_commit: 61a7f3a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4Core_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Core_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Core_1.44.0.tgz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 19 Package: a4Preproc Version: 1.44.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: b5b292010bb2569aa812741f4817a9cf NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: RELEASE_3_15 git_last_commit: 2523812 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4Preproc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Preproc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Preproc_1.44.0.tgz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 6 Package: a4Reporting Version: 1.44.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 6bd6466049714fce32cc0cc7f9d798b9 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: RELEASE_3_15 git_last_commit: bfe8350 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/a4Reporting_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Reporting_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Reporting_1.44.0.tgz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABarray Version: 1.64.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 9100b15a2c28291a041b8362428b4a0a NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: RELEASE_3_15 git_last_commit: 4c608c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ABarray_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ABarray_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ABarray_1.64.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: abseqR Version: 1.14.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: ec8e962a9aafb93b5f3d44145b782d4b NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: RELEASE_3_15 git_last_commit: 0243554 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/abseqR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/abseqR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/abseqR_1.14.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 111 Package: ABSSeq Version: 1.50.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: bee12a5e2276511093895d707361beea NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_15 git_last_commit: 4f384d0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ABSSeq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ABSSeq_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ABSSeq_1.50.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 9 Package: acde Version: 1.26.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 65cd7aad80d9e19773ee2f301732f762 NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: RELEASE_3_15 git_last_commit: dfef9a4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/acde_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/acde_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/acde_1.26.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R dependencyCount: 3 Package: ACE Version: 1.14.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: ee25f2e6cbaca43265d20b8a5c009b5c NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: RELEASE_3_15 git_last_commit: 5cf2d32 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ACE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ACE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ACE_1.14.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 79 Package: aCGH Version: 1.74.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: x64 MD5sum: 6378d175c6c0a188f7895a59abf55373 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: RELEASE_3_15 git_last_commit: e7ba380 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/aCGH_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aCGH_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aCGH_1.74.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP dependencyCount: 16 Package: ACME Version: 2.52.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: x64 MD5sum: 3aa1920a5dbe9c7cce332538a39bbc81 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: RELEASE_3_15 git_last_commit: 14a97c7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ACME_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ACME_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ACME_2.52.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 6 Package: ADaCGH2 Version: 2.36.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: x64 MD5sum: 324be393fe52409259fcda3a27bc93a1 NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan. Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_15 git_last_commit: faae5aa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ADaCGH2_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADaCGH2_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADaCGH2_2.36.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 98 Package: ADAM Version: 1.12.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 08a72855f9dad62c53d3ec635f6ef002 NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression, Microarray Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_15 git_last_commit: 994e420 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ADAM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADAM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADAM_1.12.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 83 Package: ADAMgui Version: 1.12.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: markdown, BiocStyle License: GPL (>= 2) MD5sum: 83ad813346efaba414a4076fdb503a8b NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: RELEASE_3_15 git_last_commit: 6f63302 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ADAMgui_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADAMgui_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADAMgui_1.12.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 159 Package: adductomicsR Version: 1.12.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 MD5sum: 12890e7a28e6d87eb3a262017f3f6c95 NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: RELEASE_3_15 git_last_commit: 3fd767a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/adductomicsR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/adductomicsR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/adductomicsR_1.12.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 137 Package: ADImpute Version: 1.6.0 Depends: R (>= 4.0) Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 94c0ad954e126357f2c298cd65a11d23 NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] () Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: RELEASE_3_15 git_last_commit: 1685f60 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ADImpute_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADImpute_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADImpute_1.6.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 53 Package: adSplit Version: 1.66.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: x64 MD5sum: b209dc79a5ac8650b753986f278b95c0 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: RELEASE_3_15 git_last_commit: 64580a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/adSplit_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/adSplit_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/adSplit_1.66.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 54 Package: AffiXcan Version: 1.14.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 600bb8b130bca53a23db75d00b5cc90c NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: RELEASE_3_15 git_last_commit: 135889a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AffiXcan_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffiXcan_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffiXcan_1.14.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 56 Package: affxparser Version: 1.68.1 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: x64 MD5sum: ada566b625f0b6675ddaa15a24deaeda NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: RELEASE_3_15 git_last_commit: 821a01a git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-29 source.ver: src/contrib/affxparser_1.68.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/affxparser_1.68.1.zip mac.binary.ver: bin/macosx/contrib/4.2/affxparser_1.68.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.74.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: x64 MD5sum: 44096f967d9c53d05ef718da0da1ef85 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_15 git_last_commit: 2266c4a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affy_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affy_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affy_1.74.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, bgx, Cormotif, DrugVsDisease, ExiMiR, farms, frmaTools, gcrma, logitT, maskBAD, panp, prebs, qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, sscore, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, ccTutorial, CLL, curatedBladderData, curatedOvarianData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, Cormotif, crossmeta, Doscheda, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred, IsoGene, seeker suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray, beadarraySNP, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 11 Package: affycomp Version: 1.72.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: bc78a7172802ac8e93ed8dcd3889125f NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_15 git_last_commit: c52baea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affycomp_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affycomp_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affycomp_1.72.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 6 Package: AffyCompatible Version: 1.56.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 51595bbb66cdc85fd10d368e84b8b2e6 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/AffyCompatible git_branch: RELEASE_3_15 git_last_commit: 37ea4bb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AffyCompatible_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffyCompatible_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffyCompatible_1.56.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R dependencyCount: 19 Package: affyContam Version: 1.54.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 2b8bde38890e96194d21e7e37f1e017c NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: RELEASE_3_15 git_last_commit: c5208b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affyContam_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyContam_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyContam_1.54.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R importsMe: arrayMvout dependencyCount: 14 Package: affycoretools Version: 1.68.1 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 5c2be5684a35036d1bda27c27bf72ca5 NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_15 git_last_commit: 69546b1 git_last_commit_date: 2022-05-10 Date/Publication: 2022-05-15 source.ver: src/contrib/affycoretools_1.68.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/affycoretools_1.68.1.zip mac.binary.ver: bin/macosx/contrib/4.2/affycoretools_1.68.1.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R suggestsMe: EnMCB dependencyCount: 191 Package: affyILM Version: 1.48.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: c1b4238ce39976e6125c0e7345cb303c NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: RELEASE_3_15 git_last_commit: 4603a4c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affyILM_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyILM_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyILM_1.48.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 26 Package: affyio Version: 1.66.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: x64 MD5sum: d61dec136e65390fdd17c1245cb9b21f NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: RELEASE_3_15 git_last_commit: 3a0b907 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affyio_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyio_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyio_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.70.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: d4c72b35fe7167898588bf04fa931cc2 NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: RELEASE_3_15 git_last_commit: a56a921 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affylmGUI_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affylmGUI_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affylmGUI_1.70.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 57 Package: affyPLM Version: 1.72.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: x64 MD5sum: 52191fd221979f4fe16c0483ab4fa0bb NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: RELEASE_3_15 git_last_commit: 394c0a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/affyPLM_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyPLM_1.71.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyPLM_1.72.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: RefPlus, bapred importsMe: affylmGUI, arrayQualityMetrics, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 25 Package: AffyRNADegradation Version: 1.42.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: c4604cc02f9b44c9523c26ffa1c3d374 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: RELEASE_3_15 git_last_commit: 5775f41 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AffyRNADegradation_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffyRNADegradation_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffyRNADegradation_1.42.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 12 Package: AGDEX Version: 1.44.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 5f4bdf65af4cda2dbc35c945a3c7d428 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: RELEASE_3_15 git_last_commit: 9d3eb90 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AGDEX_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AGDEX_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AGDEX_1.44.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 50 Package: aggregateBioVar Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: 61f11d2bcb851250747f97ac2100a0c6 NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: RELEASE_3_15 git_last_commit: acfd224 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/aggregateBioVar_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aggregateBioVar_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aggregateBioVar_1.6.0.tgz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 37 Package: agilp Version: 3.28.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: e1fd01747e0a9b7f502308e2ac9aecda NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: RELEASE_3_15 git_last_commit: 2c6dfcc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/agilp_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/agilp_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/agilp_3.28.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.46.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 6b3a4715fa0c6b64b107bf21863a691c NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: RELEASE_3_15 git_last_commit: 8c6d73e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AgiMicroRna_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AgiMicroRna_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AgiMicroRna_2.46.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 192 Package: AIMS Version: 1.28.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ee07e32292e7678e5d3e2fd51b99771d NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: RELEASE_3_15 git_last_commit: 84608df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AIMS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AIMS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AIMS_1.28.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 11 Package: airpart Version: 1.4.0 Depends: R (>= 4.1) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater, stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue, dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2, ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid, grDevices, graphics, utils, pbapply Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0), gplots, tidyr License: GPL-2 MD5sum: 47cc486da108e738420753c3dd62b560 NeedsCompilation: no Title: Differential cell-type-specific allelic imbalance Description: Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets. biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing, GeneRegulation, GeneExpression, Transcription, TranscriptomeVariant, CellBiology, FunctionalGenomics, DifferentialExpression, GraphAndNetwork, Regression, Clustering, QualityControl Author: Wancen Mu [aut, cre] (), Michael Love [aut, ctb] () Maintainer: Wancen Mu URL: https://github.com/Wancen/airpart VignetteBuilder: knitr BugReports: https://github.com/Wancen/airpart/issues git_url: https://git.bioconductor.org/packages/airpart git_branch: RELEASE_3_15 git_last_commit: 5ccd910 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/airpart_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/airpart_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/airpart_1.4.0.tgz vignettes: vignettes/airpart/inst/doc/airpart.html vignetteTitles: Differential allelic imbalance with airpart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/airpart/inst/doc/airpart.R dependencyCount: 123 Package: ALDEx2 Version: 1.28.1 Depends: methods, stats, zCompositions, Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: file LICENSE MD5sum: c64bc133eab92b0211fa47fd9362cf77 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: RELEASE_3_15 git_last_commit: f8d8ba6 git_last_commit_date: 2022-05-03 Date/Publication: 2022-05-15 source.ver: src/contrib/ALDEx2_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ALDEx2_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ALDEx2_1.28.1.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R dependsOnMe: omicplotR importsMe: benchdamic, microbiomeMarker, aIc suggestsMe: propr dependencyCount: 46 Package: alevinQC Version: 1.12.1 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2, GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager License: MIT + file LICENSE Archs: x64 MD5sum: e6bc518f0fd4391236394568f2ed43ce NeedsCompilation: yes Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_15 git_last_commit: e556ff5 git_last_commit_date: 2022-05-21 Date/Publication: 2022-05-22 source.ver: src/contrib/alevinQC_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/alevinQC_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/alevinQC_1.12.1.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 90 Package: AllelicImbalance Version: 1.34.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: b557f85e27b7cc4d443676dcbdc3fad1 NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: RELEASE_3_15 git_last_commit: 290708c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AllelicImbalance_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AllelicImbalance_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AllelicImbalance_1.34.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 151 Package: AlphaBeta Version: 1.10.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f09fa46f51b104a6e4ab12f809ea7505 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: RELEASE_3_15 git_last_commit: 58b5c92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AlphaBeta_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AlphaBeta_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AlphaBeta_1.10.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 79 Package: alpine Version: 1.22.0 Depends: R (>= 3.5.0) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, markdown, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 377e2abb336e08e702f6c2d729a03740 NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alpine git_branch: RELEASE_3_15 git_last_commit: 6107a82 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/alpine_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/alpine_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/alpine_1.22.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R dependencyCount: 102 Package: AlpsNMR Version: 3.6.1 Depends: R (>= 4.1), dplyr (>= 0.7.5), future (>= 1.10.0), magrittr (>= 1.5) Imports: utils, graphics, stats, grDevices, signal (>= 0.7-6), rlang (>= 0.3.0.1), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), readxl (>= 1.1.0), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.3.2), matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0), BiocParallel Suggests: DT (>= 0.5), testthat (>= 2.0.0), plotly (>= 4.7.1), ChemoSpec, knitr, zip (>= 2.0.4), GGally (>= 1.4.0), ggrepel (>= 0.8.0), writexl (>= 1.0), curl, progressr, SummarizedExperiment, S4Vectors License: MIT + file LICENSE MD5sum: 6c96ba38c9c95f34ca4f61f9280ac2a4 NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre] (), Francisco Madrid Gambin [aut] (), Luis Fernandez [aut] (), Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Sergio Oller Moreno URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR VignetteBuilder: knitr BugReports: https://github.com/sipss/AlpsNMR/issues git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_15 git_last_commit: 11d5603 git_last_commit_date: 2022-09-05 Date/Publication: 2022-09-06 source.ver: src/contrib/AlpsNMR_3.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/AlpsNMR_3.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/AlpsNMR_3.6.1.tgz vignettes: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.html vignetteTitles: Introduction to AlpsNMR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.R dependencyCount: 131 Package: altcdfenvs Version: 2.58.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 58d6847704dab9e8fa271124e510cf17 NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: RELEASE_3_15 git_last_commit: 08255a7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/altcdfenvs_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/altcdfenvs_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/altcdfenvs_2.58.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight dependencyCount: 26 Package: AMARETTO Version: 1.12.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr License: Apache License (== 2.0) + file LICENSE MD5sum: 4d7337ced09fe9a98a4ae93d4cd23d1a NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: RELEASE_3_15 git_last_commit: a2f1b61 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AMARETTO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AMARETTO_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AMARETTO_1.12.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 156 Package: AMOUNTAIN Version: 1.22.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: fa8082bc0f4f22cdc72dcf1a8cac8b99 NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: RELEASE_3_15 git_last_commit: bf457d6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AMOUNTAIN_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AMOUNTAIN_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AMOUNTAIN_1.22.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.18.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), data.table (>= 1.10.4-3) Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>= 1.2.7) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: x64 MD5sum: fc3fb682fb7bb746d2fdc4525bb7a2e4 NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: RELEASE_3_15 git_last_commit: 51c279f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/amplican_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/amplican_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/amplican_1.18.0.tgz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 108 Package: Anaquin Version: 2.20.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 62a8dded859e0aeede3527f9d08bc396 NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: RELEASE_3_15 git_last_commit: 61598dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Anaquin_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Anaquin_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Anaquin_2.20.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 108 Package: ANCOMBC Version: 1.6.4 Depends: R (>= 4.2.0) Imports: mia, stats, CVXR, DescTools, Hmisc, MASS, Rdpack, S4Vectors, SingleCellExperiment, SummarizedExperiment, doParallel, doRNG, dplyr, emmeans, energy, foreach, lme4, lmerTest, magrittr, nloptr, parallel, rlang, rngtools, tibble, tidyr, utils Suggests: knitr, rmarkdown, testthat, DT, caret, microbiome, tidyverse License: Artistic-2.0 MD5sum: 54385e1df1e56b17734175c9cdbb3536 NeedsCompilation: no Title: Microbiome differential abudance and correlation analyses with bias correction Description: ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] () Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues git_url: https://git.bioconductor.org/packages/ANCOMBC git_branch: RELEASE_3_15 git_last_commit: fa7626a git_last_commit_date: 2022-10-17 Date/Publication: 2022-10-18 source.ver: src/contrib/ANCOMBC_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/ANCOMBC_1.6.4.zip mac.binary.ver: bin/macosx/contrib/4.2/ANCOMBC_1.6.4.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html, vignettes/ANCOMBC/inst/doc/ANCOMBC.html, vignettes/ANCOMBC/inst/doc/ANCOMBC2.html, vignettes/ANCOMBC/inst/doc/SECOM.html vignetteTitles: ANCOM, ANCOMBC, ANCOMBC2, SECOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOM.R, vignettes/ANCOMBC/inst/doc/ANCOMBC.R, vignettes/ANCOMBC/inst/doc/ANCOMBC2.R, vignettes/ANCOMBC/inst/doc/SECOM.R importsMe: benchdamic, microbiomeMarker dependencyCount: 205 Package: AneuFinder Version: 1.24.0 Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, reshape2, ggdendro, ggrepel, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: x64 MD5sum: 7aeaf4eb6ede4839a9ef8259534c8a38 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: ImmunoOncology, Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: RELEASE_3_15 git_last_commit: 4c6906e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AneuFinder_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AneuFinder_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AneuFinder_1.24.0.tgz vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R dependencyCount: 88 Package: ANF Version: 1.18.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: afca744930d5b330977062d3e7c54486 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: RELEASE_3_15 git_last_commit: 865b023 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ANF_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ANF_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ANF_1.18.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 18 Package: animalcules Version: 1.12.0 Depends: R (>= 4.0.0) Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2, rentrez, reshape2, covr, ape, vegan, dplyr, magrittr, MultiAssayExperiment, SummarizedExperiment, S4Vectors (>= 0.23.19), XML, forcats, scales, lattice, glmnet, tsne, plotROC, DT, utils, limma, methods, stats, tibble, biomformat, umap, Matrix, GUniFrac Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 3066543e2dc507272a4bb5fa6a766447 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Yue Zhao [aut, cre] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Yue Zhao URL: https://github.com/compbiomed/animalcules VignetteBuilder: knitr BugReports: https://github.com/compbiomed/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_15 git_last_commit: 7f75684 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/animalcules_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/animalcules_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/animalcules_1.12.0.tgz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R dependencyCount: 194 Package: annaffy Version: 1.68.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 21878a5bc4e0f4029f8f64b70b130659 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: RELEASE_3_15 git_last_commit: fa930c0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/annaffy_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annaffy_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annaffy_1.68.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: metaMA dependencyCount: 47 Package: annmap Version: 1.38.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: 088b03093df6b99e12c026f151a920cb NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. The annmap database can be downloaded from: https://figshare.manchester.ac.uk/account/articles/16685071 biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_15 git_last_commit: 291788d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/annmap_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/annmap_1.38.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 68 Package: annotate Version: 1.74.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: 9ea0f29f89f254a7ba715ec8f84b2509 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_15 git_last_commit: 200c717 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/annotate_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotate_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotate_1.74.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats, lumi, methylumi, MGFR, phenoTest, qpgraph, RpsiXML, tigre, UMI4Cats, easyDifferentialGeneCoexpression, geneExpressionFromGEO, GOxploreR suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pageRank, pcxn, PhosR, RnBeads, siggenes, SummarizedExperiment, systemPipeR, adme16cod.db, ag.db, ath1121501.db, bovine.db, canine.db, canine2.db, celegans.db, chicken.db, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, drosgenome1.db, drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db, h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db, hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db, hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, htmg430a.db, htmg430b.db, htmg430pm.db, htrat230pm.db, htratfocus.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db, hu35ksubd.db, hu6800.db, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiMouseAll.db, lumiRatAll.db, m10kcod.db, m20kcod.db, mgu74a.db, mgu74av2.db, mgu74b.db, mgu74bv2.db, mgu74c.db, mgu74cv2.db, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430b.db, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse430a2.db, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubb.db, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, mwgcod.db, Norway981.db, nugohs1a520180.db, nugomm1a520177.db, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, POCRCannotation.db, porcine.db, r10kcod.db, rae230a.db, rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI, MOSS dependencyCount: 47 Package: AnnotationDbi Version: 1.58.0 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: 0ca49eec42d351003026dd1da6dcf8b6 NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: RELEASE_3_15 git_last_commit: 05fcf7a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AnnotationDbi_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationDbi_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationDbi_1.58.0.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category, ChromHeatMap, customProDB, deco, DEXSeq, EGSEA, EpiTxDb, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, soybeanprobe, sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, bioCancer, BiocSet, biomaRt, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, compEpiTools, conclus, consensusDE, crisprseekplus, CrispRVariants, crossmeta, cTRAP, debrowser, derfinder, DominoEffect, DOSE, EDASeq, eegc, EnrichmentBrowser, ensembldb, erma, esATAC, FRASER, GA4GHshiny, gage, GAPGOM, genefilter, geneplotter, GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR, ggbio, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gpart, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, interactiveDisplay, IRISFGM, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, MLP, MSnID, multiGSEA, multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, netOmics, NetSAM, netZooR, ontoProc, ORFik, Organism.dplyr, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, pwOmics, qpgraph, QuasR, ReactomePA, REDseq, regutools, restfulSE, rgsepd, ribosomeProfilingQC, RNAAgeCalc, RpsiXML, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch, simplifyEnrichment, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, TFutils, tigre, trackViewer, trena, TRESS, tricycle, txcutr, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, ppiData, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, jetset, Mega2R, MetaIntegrator, netgsa, pathfindR, prioGene, pulseTD, RobLoxBioC, seeker, WGCNA suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics, BiocOncoTK, BioPlex, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR, enrichplot, epimutacions, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gCrisprTools, GeneRegionScan, GenomicRanges, iSEEu, limma, MutationalPatterns, oligo, OUTRIDER, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, RLSeq, sigPathway, sparrow, SummarizedExperiment, systemPipeR, tidybulk, topconfects, weitrix, wiggleplotr, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, cRegulome, DGCA, dnapath, easylabel, genekitr, pagoda2, Platypus, rliger, scITD dependencyCount: 44 Package: AnnotationFilter Version: 1.20.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 02a5abcb3e6d830b25030c3f17e234c6 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: RELEASE_3_15 git_last_commit: 2818aff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AnnotationFilter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationFilter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationFilter_1.20.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, CompoundDb, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, drugTargetInteractions, ggbio, QFeatures, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, RNAseqQC, utr.annotation suggestsMe: dasper, GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 17 Package: AnnotationForge Version: 1.38.1 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db, markdown, BiocStyle, knitr, BiocManager, BiocFileCache License: Artistic-2.0 MD5sum: 903b1836aad1d0d602a85024e44c5c82 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_15 git_last_commit: 2dcedf3 git_last_commit_date: 2022-08-10 Date/Publication: 2022-08-11 source.ver: src/contrib/AnnotationForge_1.38.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationForge_1.38.1.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationForge_1.38.1.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 46 Package: AnnotationHub Version: 3.4.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 51942d3a3089fd86bb9523326930d36a NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_15 git_last_commit: e74e54c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AnnotationHub_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationHub_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationHub_3.4.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, LRcell, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, rGenomeTracksData, synaptome.data, UCSCRepeatMasker, MetaGxBreast, MetaGxOvarian, NestLink, sesameData, tartare, annotation, sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows importsMe: annotatr, atena, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, dmrseq, EpiCompare, GenomicScores, GSEABenchmarkeR, gwascat, MACSr, meshes, MethReg, MSnID, NxtIRFcore, OGRE, ontoProc, psichomics, pwOmics, regutools, REMP, restfulSE, RLSeq, scanMiRApp, scAnnotatR, scmeth, scTensor, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, grasp2db, metaboliteIDmapping, synaptome.db, adductData, alpineData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, easierData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GenomicDistributionsData, HCAData, HMP16SData, HMP2Data, mcsurvdata, MetaGxPancreas, msigdb, RLHub, scpdata, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TCGAWorkflow, RNAseqQC, utr.annotation suggestsMe: BgeeCall, BioPlex, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, DNAshapeR, dupRadar, ELMER, ensembldb, epimutacions, epiNEM, EpiTxDb, epivizrChart, epivizrData, GenomicRanges, Glimma, GOSemSim, maser, MIRA, MSnbase, multicrispr, nullranges, OrganismDbi, plotgardener, recountmethylation, satuRn, TCGAbiolinks, TCGAutils, VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, HarmonizedTCGAData, SingleRBook dependencyCount: 85 Package: AnnotationHubData Version: 1.26.1 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 7af5251e67d3e3c8a2f899a23262d3ed NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_15 git_last_commit: a04119c git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-28 source.ver: src/contrib/AnnotationHubData_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationHubData_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationHubData_1.26.1.tgz vignettes: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHEnsDbs, EuPathDB suggestsMe: HubPub, GenomicState dependencyCount: 131 Package: annotationTools Version: 1.70.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: 871a1964e3d11fe35e19b2c25a61d2bb NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: RELEASE_3_15 git_last_commit: 29fdba7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/annotationTools_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotationTools_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotationTools_1.70.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R dependencyCount: 6 Package: annotatr Version: 1.22.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.23.10), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: 9015c0315c7705302cf10635826646be NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: RELEASE_3_15 git_last_commit: dde0987 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/annotatr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotatr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotatr_1.22.0.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: annotatr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth suggestsMe: borealis, ramr dependencyCount: 142 Package: anota Version: 1.44.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 6defc6ab6549091a0b6452d3936724d6 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: RELEASE_3_15 git_last_commit: 504c597 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/anota_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/anota_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/anota_1.44.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 48 Package: anota2seq Version: 1.18.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: 6254b9610d7118fd3fb4986b8fe8366d NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Julie Lorent VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_15 git_last_commit: 6c05999 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/anota2seq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/anota2seq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/anota2seq_1.18.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 101 Package: antiProfiles Version: 1.36.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 909e77bdc27ce98fbae342ff992b5930 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: RELEASE_3_15 git_last_commit: f75cea8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/antiProfiles_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/antiProfiles_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/antiProfiles_1.36.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVIL Version: 1.8.7 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), tibble, tidyselect, tidyr, rlang, BiocManager Suggests: parallel, knitr, rmarkdown, testthat, withr, readr, BiocStyle License: Artistic-2.0 MD5sum: ca8fcc3c4246b011d25c33e3224e60d1 NeedsCompilation: no Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and developer functionality. For the end-user, AnVIL provides fast binary package installation, utitlities for working with Terra / AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programatic access to the Terra, Leonardo, Rawls, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (), Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Marcel Ramos [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: RELEASE_3_15 git_last_commit: 4efdba0 git_last_commit_date: 2022-10-06 Date/Publication: 2022-10-09 source.ver: src/contrib/AnVIL_1.8.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVIL_1.8.7.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVIL_1.8.7.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html, vignettes/AnVIL/inst/doc/RunningWorkflow.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package, Running an AnVIL workflow within R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R, vignettes/AnVIL/inst/doc/RunningWorkflow.R dependsOnMe: cBioPortalData, terraTCGAdata importsMe: AnVILPublish dependencyCount: 40 Package: AnVILBilling Version: 1.6.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: bcbefba9776f07db017fbd4aaf8944e1 NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: RELEASE_3_15 git_last_commit: 48e30c6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AnVILBilling_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVILBilling_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVILBilling_1.6.0.tgz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 91 Package: AnVILPublish Version: 1.6.0 Imports: AnVIL, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, BiocManager, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 1fe612f55f42351ab9fee715609ae161 NeedsCompilation: no Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Martin Morgan [aut, cre] (), Vincent Carey [ctb] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: RELEASE_3_15 git_last_commit: 184ed3b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AnVILPublish_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVILPublish_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVILPublish_1.6.0.tgz vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html vignetteTitles: Publishing R / Bioconductor packages to AnVIL Workspaces hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R dependencyCount: 71 Package: APAlyzer Version: 1.10.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools, HybridMTest Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 MD5sum: bb8744f645ffdaa03f9b62c005307f93 NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (), Bin Tian [aut], Wei-Chun Chen [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: RELEASE_3_15 git_last_commit: 0545bb5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/APAlyzer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/APAlyzer_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/APAlyzer_1.10.0.tgz vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R dependencyCount: 134 Package: apComplex Version: 2.62.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 2e0575cd448d9dd3f795cc7d3b2c050f NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: RELEASE_3_15 git_last_commit: 0661f03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/apComplex_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/apComplex_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/apComplex_2.62.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependencyCount: 51 Package: apeglm Version: 1.18.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 4eb5beda340aca0246bcb92417279f4e NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_15 git_last_commit: 961f6ef git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/apeglm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/apeglm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/apeglm_1.18.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: airpart, debrowser, DiffBind, TEKRABber suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq, RNAseqQC dependencyCount: 36 Package: APL Version: 1.0.1 Depends: R (>= 4.2) Imports: reticulate, ggrepel, ggplot2, viridisLite, plotly, Seurat, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, testthat License: GPL (>= 3) MD5sum: f159e07074cb747859adfe83772faf84 NeedsCompilation: no Title: Association Plots Description: APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest. biocViews: StatisticalMethod, DimensionReduction, SingleCell, Sequencing, RNASeq, GeneExpression Author: Elzbieta Gralinska [cre, aut], Clemens Kohl [aut], Martin Vingron [aut] Maintainer: Elzbieta Gralinska SystemRequirements: python, pytorch, numpy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: RELEASE_3_15 git_last_commit: 88cc955 git_last_commit_date: 2022-10-14 Date/Publication: 2022-10-16 source.ver: src/contrib/APL_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/APL_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/APL_1.0.1.tgz vignettes: vignettes/APL/inst/doc/APL.html vignetteTitles: Analyzing data with APL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APL/inst/doc/APL.R dependencyCount: 176 Package: appreci8R Version: 1.14.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, rsnps, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, utils, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db License: LGPL-3 MD5sum: 92f0cbf82e5a176b5d119638276fc424 NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: RELEASE_3_15 git_last_commit: 8f23d88 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/appreci8R_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/appreci8R_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/appreci8R_1.14.0.tgz vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 160 Package: aroma.light Version: 3.26.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: 26b78a500d96e10ae6996ce23b5cdfb3 NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_15 git_last_commit: 3aa4577 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/aroma.light_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aroma.light_3.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aroma.light_3.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.56.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 5f43ba6877a621ac184392e170dd0f52 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Suhaib Mohammed git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: RELEASE_3_15 git_last_commit: 56fb940 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ArrayExpress_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ArrayExpress_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ArrayExpress_1.56.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease, maEndToEnd importsMe: seeker suggestsMe: Hiiragi2013, bapred dependencyCount: 55 Package: ArrayExpressHTS Version: 1.46.0 Depends: sampling, Rsamtools (>= 1.99.0), snow Imports: Biobase, BiocGenerics, Biostrings, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rhtslib (>= 1.15.3) License: Artistic License 2.0 MD5sum: 3fff830726a7428ab9f91a100e83fcc6 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: ImmunoOncology, RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/ArrayExpressHTS git_branch: RELEASE_3_15 git_last_commit: 00e9ccc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ArrayExpressHTS_1.46.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ArrayExpressHTS_1.46.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R dependencyCount: 143 Package: arrayMvout Version: 1.54.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy Imports: mdqc, affyContam, lumi Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 4422c28db01431577a1943015f5a5d22 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: RELEASE_3_15 git_last_commit: 74c2866 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/arrayMvout_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayMvout_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayMvout_1.54.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 168 Package: arrayQuality Version: 1.74.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: ea3760e213d8fcde58164d5a9bf4bde2 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: RELEASE_3_15 git_last_commit: c645e30 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/arrayQuality_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayQuality_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayQuality_1.74.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.52.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 5eef09dc1d113b0993482bd1c68f0adf NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: RELEASE_3_15 git_last_commit: f868c74 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/arrayQualityMetrics_3.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayQualityMetrics_3.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayQualityMetrics_3.52.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R dependsOnMe: maEndToEnd dependencyCount: 125 Package: ARRmNormalization Version: 1.36.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 5d47287b89d24f66882ea9543733b38b NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: RELEASE_3_15 git_last_commit: 4098446 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ARRmNormalization_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ARRmNormalization_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ARRmNormalization_1.36.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.14.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table, dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR, knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 0076d69f58f85fd6281ba03bcba8bbae NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (), Alexandre Rosa Campos [aut, ctb] (), John Von Dollen [aut], Nevan Krogan [aut] (), Danielle Swaney [aut, ctb] () Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: RELEASE_3_15 git_last_commit: 6bdad67 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/artMS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/artMS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/artMS_1.14.0.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 148 Package: ASAFE Version: 1.22.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: eaa60da746e1f05badd64301db839827 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: RELEASE_3_15 git_last_commit: 8a78060 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASAFE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASAFE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASAFE_1.22.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.40.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: x64 MD5sum: 4f0c377f0d6df54dd9ef043cc30fab39 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: RELEASE_3_15 git_last_commit: 281b4fe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASEB_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASEB_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASEB_1.40.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.30.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 4164abc4e3cd8351c009d04851c6877f NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: RELEASE_3_15 git_last_commit: 5c38ec7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASGSCA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASGSCA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASGSCA_1.30.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: ASICS Version: 2.12.1 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 0f76deaba7e73c2332357db448607eff NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_15 git_last_commit: a399050 git_last_commit_date: 2022-06-27 Date/Publication: 2022-07-05 source.ver: src/contrib/ASICS_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASICS_2.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ASICS_2.12.1.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R dependencyCount: 95 Package: ASpediaFI Version: 1.10.0 Depends: R (>= 3.6.0), SummarizedExperiment, ROCR Imports: BiocParallel, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma, S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr, fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer, scales, grid, ggplot2, mGSZ, utils Suggests: knitr License: GPL-3 MD5sum: 6903082b28243b1a879f82cc8de882a5 NeedsCompilation: no Title: ASpedia-FI: Functional Interaction Analysis of Alternative Splicing Events Description: This package provides functionalities for a systematic and integrative analysis of alternative splicing events and their functional interactions. biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression, GeneSetEnrichment, GraphAndNetwork, KEGG, Network, NetworkInference, Pathways, Reactome, Transcription, Sequencing, Visualization Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr BugReports: https://github.com/nachoryu/ASpediaFI git_url: https://git.bioconductor.org/packages/ASpediaFI git_branch: RELEASE_3_15 git_last_commit: d50ff1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASpediaFI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASpediaFI_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASpediaFI_1.10.0.tgz vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf vignetteTitles: ASpediaFI.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R dependencyCount: 186 Package: ASpli Version: 2.6.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics, pbmcapply License: GPL MD5sum: 354e1aaeb3b225c846bd5ce3b29d96f0 NeedsCompilation: no Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_15 git_last_commit: 4e2d897 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASpli_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASpli_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASpli_2.6.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R dependencyCount: 168 Package: AssessORF Version: 1.14.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 2b2c07c21f53815f147803b1a8f4ef30 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: RELEASE_3_15 git_last_commit: 72fb4f6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AssessORF_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AssessORF_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AssessORF_1.14.0.tgz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R suggestsMe: AssessORFData dependencyCount: 36 Package: ASSET Version: 2.14.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 9dd6e62f38ac9f3c6b486db341db7afa NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant. biocViews: StatisticalMethod, SNP, GenomeWideAssociation, MultipleComparison Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_15 git_last_commit: 52848e7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASSET_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASSET_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASSET_2.14.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 15 Package: ASSIGN Version: 1.32.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 85171efb98ea4c0b52d6f981827fdca5 NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: RELEASE_3_15 git_last_commit: 97d2eb3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASSIGN_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASSIGN_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASSIGN_1.32.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 98 Package: ASURAT Version: 1.0.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp (>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize, grid, grDevices, graphics LinkingTo: Rcpp Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi, BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 + file LICENSE Archs: x64 MD5sum: fbc1187e5ecb6c0557c0fb42cde3f0e8 NeedsCompilation: yes Title: Functional annotation-driven unsupervised clustering for single-cell data Description: ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs). biocViews: GeneExpression, SingleCell, Sequencing, Clustering, GeneSignaling Author: Keita Iida [aut, cre] (), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASURAT git_branch: RELEASE_3_15 git_last_commit: dd4df14 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ASURAT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASURAT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASURAT_1.0.0.tgz vignettes: vignettes/ASURAT/inst/doc/ASURAT.html vignetteTitles: ASURAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASURAT/inst/doc/ASURAT.R dependencyCount: 48 Package: ATACseqQC Version: 1.20.2 Depends: R (>= 3.5.0), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) MD5sum: 1a070d58462b5816efa6ab289a59a84f NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_15 git_last_commit: 067730d git_last_commit_date: 2022-05-02 Date/Publication: 2022-05-02 source.ver: src/contrib/ATACseqQC_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ATACseqQC_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ATACseqQC_1.20.2.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R dependencyCount: 182 Package: atena Version: 1.2.2 Depends: R (>= 4.1), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, BiocParallel, S4Vectors, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats, AnnotationHub, scales Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: df7df48cdcb2e21258932e13ea3a38cc NeedsCompilation: no Title: Analysis of Transposable Elements Description: Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics Author: Beatriz Calvo-Serra [aut, cre], Robert Castelo [aut] Maintainer: Beatriz Calvo-Serra URL: https://github.com/functionalgenomics/atena VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: RELEASE_3_15 git_last_commit: d5c8a75 git_last_commit_date: 2022-09-08 Date/Publication: 2022-09-11 source.ver: src/contrib/atena_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/atena_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/atena_1.2.2.tgz vignettes: vignettes/atena/inst/doc/atena.html vignetteTitles: An introduction to the atena package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atena/inst/doc/atena.R dependencyCount: 115 Package: atSNP Version: 1.12.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 3bddc97a79d686edb1beee9fea4bb760 NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: RELEASE_3_15 git_last_commit: 39778b1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/atSNP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/atSNP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/atSNP_1.12.0.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 153 Package: attract Version: 1.48.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: 56d221fb1c97d0fa83e75bf880893e39 NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: RELEASE_3_15 git_last_commit: 79fc65c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/attract_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/attract_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/attract_1.48.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 67 Package: AUCell Version: 1.18.1 Imports: DelayedArray, DelayedMatrixStats, data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: 787c2fed47888090106c92ee706e4a97 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_15 git_last_commit: 387435b git_last_commit_date: 2022-05-19 Date/Publication: 2022-05-19 source.ver: src/contrib/AUCell_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/AUCell_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/AUCell_1.18.1.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R dependsOnMe: OSCA.basic importsMe: RcisTarget suggestsMe: decoupleR dependencyCount: 90 Package: autonomics Version: 1.4.0 Depends: R (>= 4.0) Imports: abind, assertive, BiocFileCache, BiocGenerics, colorspace, data.table, edgeR, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, pcaMethods, rappdirs, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, tidyr, tools, utils Suggests: affy, AnnotationDbi, BiocManager, diagram, GenomicRanges, GEOquery, hgu95av2.db, ICSNP, knitr, lme4, lmerTest, MASS, mixOmics, mpm, nlme, org.Hs.eg.db, org.Mm.eg.db, RCurl, remotes, rmarkdown, ropls, Rsubread, rtracklayer, seqinr, statmod, testthat License: GPL-3 MD5sum: 5206ba0c92fe0e5a902cbeb9c616f2f6 NeedsCompilation: no Title: Generifying and intuifying cross-platform omics analysis Description: This package offers a generic and intuitive solution for cross-platform omics data analysis. It has functions for import, preprocessing, exploration, contrast analysis and visualization of omics data. It follows a tidy, functional programming paradigm. biocViews: DataImport, DimensionReduction, GeneExpression, MassSpectrometry, Preprocessing, PrincipalComponent, RNASeq, Software, Transcription Author: Aditya Bhagwat [aut, cre], Shahina Hayat [aut], Anna Halama [ctb], Richard Cotton [ctb], Laure Cougnaud [ctb], Rudolf Engelke [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/autonomics VignetteBuilder: knitr BugReports: https://bitbucket.org/graumannlabtools/autonomics git_url: https://git.bioconductor.org/packages/autonomics git_branch: RELEASE_3_15 git_last_commit: 7d33898 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/autonomics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/autonomics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/autonomics_1.4.0.tgz vignettes: vignettes/autonomics/inst/doc/using_autonomics.html vignetteTitles: using_autonomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/using_autonomics.R dependencyCount: 126 Package: AWFisher Version: 1.10.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: x64 MD5sum: f323ccede108deaf9d07a0aed81f2728 NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: RELEASE_3_15 git_last_commit: 8ca1e68 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/AWFisher_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AWFisher_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AWFisher_1.10.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.4.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 70c98c5ab78075e73b7d00cd537118eb NeedsCompilation: no Title: Asymmetric Within-Sample Transformation Description: We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts. biocViews: Normalization, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph] (), Stefano Pagnotta [aut, cph] () Maintainer: Davide Risso URL: https://github.com/drisso/awst VignetteBuilder: knitr BugReports: https://github.com/drisso/awst/issues git_url: https://git.bioconductor.org/packages/awst git_branch: RELEASE_3_15 git_last_commit: 435e276 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/awst_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/awst_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/awst_1.4.0.tgz vignettes: vignettes/awst/inst/doc/awst_intro.html vignetteTitles: Introduction to awst hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/awst/inst/doc/awst_intro.R dependencyCount: 25 Package: BaalChIP Version: 1.22.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: d3d1264e42688aea6285a3e480e70cad NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: RELEASE_3_15 git_last_commit: 273bdf2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BaalChIP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BaalChIP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BaalChIP_1.22.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 114 Package: BAC Version: 1.56.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: x64 MD5sum: c71f44b5daf38ddcbd3f6717c1818f83 NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/BAC git_branch: RELEASE_3_15 git_last_commit: 78f05f1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BAC_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BAC_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BAC_1.56.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R dependencyCount: 0 Package: bacon Version: 1.24.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: x64 MD5sum: 983d7f6d9b43f2a6e924695d820429a7 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: RELEASE_3_15 git_last_commit: 8e0c070 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bacon_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bacon_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bacon_1.24.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 46 Package: BADER Version: 1.34.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: x64 MD5sum: fb5e416a7802206902d343290bf7d940 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: RELEASE_3_15 git_last_commit: 2f9898d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BADER_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BADER_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BADER_1.34.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.24.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: d6bec1ad7bbb0b64ddb12944c323bc7d NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: RELEASE_3_15 git_last_commit: e2d9a3f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BadRegionFinder_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BadRegionFinder_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BadRegionFinder_1.24.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 99 Package: BAGS Version: 2.36.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: x64 MD5sum: 242b1e815deec404e55c9d2052297285 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: RELEASE_3_15 git_last_commit: 69f606f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BAGS_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BAGS_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BAGS_2.36.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 7 Package: ballgown Version: 2.28.0 Depends: R (>= 3.5.0), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr, markdown License: Artistic-2.0 MD5sum: 7dc6638e1c77c84e6e6b00e41ba0de02 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: RELEASE_3_15 git_last_commit: bedd5b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ballgown_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ballgown_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ballgown_2.28.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R dependsOnMe: VaSP suggestsMe: polyester, variancePartition dependencyCount: 83 Package: bambu Version: 2.2.0 Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), BSgenome, IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE Archs: x64 MD5sum: 1acfbe47f4f6c70d864e8def8134e9a4 NeedsCompilation: yes Title: Reference-guided isoform reconstruction and quantification for long read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: RELEASE_3_15 git_last_commit: 90b4168 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bambu_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bambu_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bambu_2.2.0.tgz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R importsMe: FLAMES suggestsMe: NanoporeRNASeq dependencyCount: 103 Package: bamsignals Version: 1.28.0 Depends: R (>= 3.5.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 712a10903a26faf476156bdd37514466 NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: RELEASE_3_15 git_last_commit: 27b70be git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bamsignals_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bamsignals_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bamsignals_1.28.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, chromstaR, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 18 Package: BANDITS Version: 1.12.0 Depends: R (>= 4.0.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) Archs: x64 MD5sum: c36df864d05e66a47f64bf2429f708af NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_15 git_last_commit: 8c159c4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BANDITS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BANDITS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BANDITS_1.12.0.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R importsMe: DifferentialRegulation dependencyCount: 76 Package: bandle Version: 1.0.0 Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang LinkingTo: Rcpp, RcppArmadillo, BH Suggests: coda (>= 0.19-4), testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Archs: x64 MD5sum: 1da1eb92caa4b81f385480d2b7f54d2f NeedsCompilation: yes Title: An R package to for the Bayesian analysis of differential subcellular localisation experiments Description: The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental method supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data. biocViews: Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry Author: Oliver M. Crook [aut, cre] (), Lisa Breckels [aut] Maintainer: Oliver M. Crook URL: http://github.com/ococrook/bandle VignetteBuilder: knitr BugReports: https://github.com/ococrook/bandle/issues git_url: https://git.bioconductor.org/packages/bandle git_branch: RELEASE_3_15 git_last_commit: a10b7c7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bandle_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bandle_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bandle_1.0.0.tgz vignettes: vignettes/bandle/inst/doc/v01-getting-started.html, vignettes/bandle/inst/doc/v02-workflow.html vignetteTitles: Analysing differential localisation experiments with BANDLE: Vignette 1, Analysing differential localisation experiments with BANDLE: Vignette 2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bandle/inst/doc/v01-getting-started.R, vignettes/bandle/inst/doc/v02-workflow.R dependencyCount: 220 Package: banocc Version: 1.20.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 90b3a7443ee7a1354a65a954c6fdcedd NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: RELEASE_3_15 git_last_commit: 5dc8a82 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/banocc_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/banocc_1.20.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 63 Package: barcodetrackR Version: 1.4.0 Depends: R (>= 4.1) Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges, graphics, grDevices, magrittr, plyr, proxy, RColorBrewer, rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors, tibble, tidyr, vegan, viridis, utils Suggests: BiocStyle, knitr, magick, rmarkdown, testthat License: file LICENSE MD5sum: e00585d7b89386527a240d12b9b7957f NeedsCompilation: no Title: Functions for Analyzing Cellular Barcoding Data Description: barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies. biocViews: Software, Visualization, Sequencing Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut] Maintainer: Diego Alexander Espinoza URL: https://github.com/dunbarlabNIH/barcodetrackR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/barcodetrackR git_branch: RELEASE_3_15 git_last_commit: a72f683 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/barcodetrackR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/barcodetrackR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/barcodetrackR_1.4.0.tgz vignettes: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.html vignetteTitles: barcodetrackR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.R dependencyCount: 95 Package: basecallQC Version: 1.20.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: b95604ce42e87e385f57426e1644104d NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: RELEASE_3_15 git_last_commit: c233bf5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/basecallQC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/basecallQC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/basecallQC_1.20.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 115 Package: BaseSpaceR Version: 1.40.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 0de684382d9f5ca8dbee14670501c5c3 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: RELEASE_3_15 git_last_commit: fae3ef7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BaseSpaceR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BaseSpaceR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BaseSpaceR_1.40.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.32.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 81f75afd4646f7e9056e2737c6df0962 NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: RELEASE_3_15 git_last_commit: 90fbc23 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Basic4Cseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Basic4Cseq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Basic4Cseq_1.32.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 49 Package: BASiCS Version: 2.8.0 Depends: R (>= 4.0), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment, viridis, utils, Matrix, matrixStats, assertthat, reshape2, BiocParallel, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, magick License: GPL (>= 2) Archs: x64 MD5sum: e453424543a1ddb2341b21c6a52bec64 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut], Nils Eling [aut], Alan O'Callaghan [aut, cre], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Alan O'Callaghan URL: https://github.com/catavallejos/BASiCS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: RELEASE_3_15 git_last_commit: f891aab git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BASiCS_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BASiCS_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BASiCS_2.8.0.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R suggestsMe: splatter dependencyCount: 124 Package: BasicSTARRseq Version: 1.24.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: fc2a9c046fdd0d82afbb2bbf1a938711 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: RELEASE_3_15 git_last_commit: 9d820ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BasicSTARRseq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BasicSTARRseq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BasicSTARRseq_1.24.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 39 Package: basilisk Version: 1.8.1 Imports: utils, methods, parallel, reticulate, dir.expiry, basilisk.utils Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: 5ecaf475ca481f37828afdac58cebee3 NeedsCompilation: no Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk git_branch: RELEASE_3_15 git_last_commit: 9cac618 git_last_commit_date: 2022-08-25 Date/Publication: 2022-08-25 source.ver: src/contrib/basilisk_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/basilisk_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/basilisk_1.8.1.tgz vignettes: vignettes/basilisk/inst/doc/motivation.html vignetteTitles: Motivation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk/inst/doc/motivation.R importsMe: BiocSklearn, cbpManager, COTAN, crisprScore, dasper, densvis, FLAMES, MACSr, MOFA2, Rcwl, snifter, spatialDE, velociraptor, zellkonverter dependencyCount: 22 Package: basilisk.utils Version: 1.8.0 Imports: utils, methods, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: df7a88b6de84e725ceef2c35981ccf55 NeedsCompilation: no Title: Basilisk Installation Utilities Description: Implements utilities for installation of the basilisk package, primarily for creation of the underlying Conda instance. This allows us to avoid re-writing the same R code in both the configure script (for centrally administered R installations) and in the lazy installation mechanism (for distributed package binaries). It is highly unlikely that developers - or, heaven forbid, end-users! - will need to interact with this package directly; they should be using the basilisk package instead. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: RELEASE_3_15 git_last_commit: 8a38d2e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/basilisk.utils_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/basilisk.utils_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/basilisk.utils_1.8.0.tgz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: _basilisk_ installation utilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: basilisk dependencyCount: 5 Package: batchelor Version: 1.12.3 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 Archs: x64 MD5sum: bda1d1660fb02dd0dc5c7587b34fbe67 NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: RELEASE_3_15 git_last_commit: 94788f9 git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/batchelor_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/batchelor_1.12.3.zip mac.binary.ver: bin/macosx/contrib/4.2/batchelor_1.12.3.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: ChromSCape, mumosa, singleCellTK suggestsMe: TSCAN, bcTSNE, RaceID dependencyCount: 51 Package: BatchQC Version: 1.24.0 Depends: R (>= 3.5.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, heatmaply, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: f9a10dab92d9807a15234658b14cec8b NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_15 git_last_commit: ce8ee03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BatchQC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BatchQC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BatchQC_1.24.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R dependencyCount: 159 Package: BayesKnockdown Version: 1.22.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 3e943888bff18c72c8159ea42defa5b4 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: RELEASE_3_15 git_last_commit: 1a523fc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BayesKnockdown_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BayesKnockdown_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BayesKnockdown_1.22.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 6 Package: BayesSpace Version: 1.6.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, assertthat, mclust, RCurl, DirichletReg, xgboost, utils, ggplot2, scales, BiocFileCache, BiocSingular LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE Archs: x64 MD5sum: fcfe2b207b0a2071d777cfc9d12a1222 NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Matt Stone URL: edward130603.github.io/BayesSpace SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: RELEASE_3_15 git_last_commit: 4a877a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BayesSpace_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BayesSpace_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BayesSpace_1.6.0.tgz vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R dependencyCount: 136 Package: bayNorm Version: 1.14.0 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) Archs: x64 MD5sum: 38df84c21beb48e0757d9c17579fc7ea NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: RELEASE_3_15 git_last_commit: 5dce41f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bayNorm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bayNorm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bayNorm_1.14.0.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 48 Package: baySeq Version: 2.30.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: be168a775c4820384cd2780f05c5132e NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/baySeq git_branch: RELEASE_3_15 git_last_commit: 96bbf7d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/baySeq_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/baySeq_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/baySeq_2.30.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC importsMe: metaseqR2, riboSeqR, srnadiff suggestsMe: compcodeR dependencyCount: 25 Package: BBCAnalyzer Version: 1.26.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 78bb30dbf42b66fbc1730ea7b9eca08c NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: RELEASE_3_15 git_last_commit: 5f8f335 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BBCAnalyzer_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BBCAnalyzer_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BBCAnalyzer_1.26.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 99 Package: BCRANK Version: 1.58.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: x64 MD5sum: e82848667f4e7f62452190f63146cef1 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: RELEASE_3_15 git_last_commit: 05e6ea5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BCRANK_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BCRANK_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BCRANK_1.58.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 18 Package: bcSeq Version: 1.18.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: x64 MD5sum: 4a9f88bf0cfb3c807347fb68da7ce325 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: RELEASE_3_15 git_last_commit: 2fd5bd3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bcSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bcSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bcSeq_1.18.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 22 Package: BDMMAcorrect Version: 1.14.0 Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 5f164e793d2d9f24924bfa39a58aa4ef NeedsCompilation: yes Title: Meta-analysis for the metagenomic read counts data from different cohorts Description: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian Author: ZHENWEI DAI Maintainer: ZHENWEI DAI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BDMMAcorrect git_branch: RELEASE_3_15 git_last_commit: bc7b4c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BDMMAcorrect_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BDMMAcorrect_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BDMMAcorrect_1.14.0.tgz vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf vignetteTitles: BDMMAcorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R dependencyCount: 62 Package: beachmat Version: 2.12.0 Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array License: GPL-3 Archs: x64 MD5sum: 765776585c5fed4a5df985c5a418ffe8 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_15 git_last_commit: 3e6af14 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/beachmat_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beachmat_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beachmat_2.12.0.tgz vignettes: vignettes/beachmat/inst/doc/external.html, vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: 4. Supporting arbitrary matrix classes (v2), 2. Reading data from R matrices in C++ (v2), 1. Developer guide, 3. Writing data into R matrix objects (v2) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/external.R, vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/output.R importsMe: batchelor, BiocSingular, DropletUtils, mumosa, scater, scran, scuttle, SingleR suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2 linksToMe: BiocSingular, bsseq, DropletUtils, glmGamPoi, mbkmeans, PCAtools, scran, scuttle, SingleR dependencyCount: 16 Package: beadarray Version: 2.46.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: x64 MD5sum: be79c641d061c8769e0f0312841f2344 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: RELEASE_3_15 git_last_commit: 086626e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/beadarray_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beadarray_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beadarray_2.46.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R dependsOnMe: beadarrayExampleData importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI dependencyCount: 80 Package: beadarraySNP Version: 1.62.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 1cbbc86e63428443856e03b771c6decf NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/beadarraySNP git_branch: RELEASE_3_15 git_last_commit: 2284b70 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/beadarraySNP_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beadarraySNP_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beadarraySNP_1.62.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R dependencyCount: 17 Package: BeadDataPackR Version: 1.48.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: x64 MD5sum: e8ca4fe8c421e04509063ed35d7a6046 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: RELEASE_3_15 git_last_commit: 8a01fe4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BeadDataPackR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BeadDataPackR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BeadDataPackR_1.48.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEARscc Version: 1.16.0 Depends: R (>= 3.5.0) Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: 770391439af906d14fed4b5aa8c932e5 NeedsCompilation: no Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BEARscc git_branch: RELEASE_3_15 git_last_commit: 8ecdaa0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BEARscc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEARscc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BEARscc_1.16.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R dependencyCount: 56 Package: BEAT Version: 1.34.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 2ab0ff53b6d665fa95ca254d94929906 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: RELEASE_3_15 git_last_commit: 586dd95 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BEAT_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEAT_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BEAT_1.34.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 57 Package: BEclear Version: 2.12.1 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, abind, stats, graphics, utils, methods, dixonTest, ids LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave License: GPL-3 Archs: x64 MD5sum: ba16b665ac281d7e45f317ca713f0928 NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: David Rasp [aut, cre] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: David Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_15 git_last_commit: 9cb383c git_last_commit_date: 2022-09-27 Date/Publication: 2022-10-04 source.ver: src/contrib/BEclear_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEclear_2.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BEclear_2.12.1.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 29 Package: beer Version: 1.0.0 Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags Imports: cli, edgeR, BiocParallel, methods, progressr, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, covr, knitr, rmarkdown, dplyr, ggplot2, spelling License: MIT + file LICENSE MD5sum: d1e6d2ffd1e2f933cfc1d860ba972f0e NeedsCompilation: no Title: Bayesian Enrichment Estimation in R Description: BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses. biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Athena Chen [aut, cre] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen URL: https://github.com/athchen/beer/ SystemRequirements: JAGS (4.3.0) VignetteBuilder: knitr BugReports: https://github.com/athchen/beer/issues git_url: https://git.bioconductor.org/packages/beer git_branch: RELEASE_3_15 git_last_commit: 4487873 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/beer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beer_1.0.0.tgz vignettes: vignettes/beer/inst/doc/beer.html vignetteTitles: beer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beer/inst/doc/beer.R dependencyCount: 81 Package: benchdamic Version: 1.2.5 Depends: R (>= 4.2.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, SummarizedExperiment, MAST, Seurat, ANCOMBC, NOISeq, dearseq, metagenomeSeq, MGLM, ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges, graphics, cowplot, tidytext Suggests: knitr, rmarkdown, kableExtra, BiocStyle, SPsimSeq, testthat License: Artistic-2.0 MD5sum: 65af54c187e67d6bc956056f92223e2d NeedsCompilation: no Title: Benchmark of differential abundance methods on microbiome data Description: Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization. biocViews: Metagenomics, Microbiome, DifferentialExpression, MultipleComparison, Normalization, Preprocessing, Software Author: Matteo Calgaro [aut, cre], Chiara Romualdi [aut], Davide Risso [aut], Nicola Vitulo [aut] Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues git_url: https://git.bioconductor.org/packages/benchdamic git_branch: RELEASE_3_15 git_last_commit: a9a5cda git_last_commit_date: 2022-09-10 Date/Publication: 2022-09-11 source.ver: src/contrib/benchdamic_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/benchdamic_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.2/benchdamic_1.2.5.tgz vignettes: vignettes/benchdamic/inst/doc/intro.html vignetteTitles: Intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/benchdamic/inst/doc/intro.R dependencyCount: 313 Package: BgeeCall Version: 1.12.2 Depends: R (>= 3.6) Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5 Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 + file LICENSE MD5sum: bef39052bfe94e08ad4c1b7df619bb4b NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: RELEASE_3_15 git_last_commit: 7ea77a9 git_last_commit_date: 2022-08-25 Date/Publication: 2022-08-25 source.ver: src/contrib/BgeeCall_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BgeeCall_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BgeeCall_1.12.2.tgz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 108 Package: BgeeDB Version: 2.22.3 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 53309dd517cce6e04d3a6c5b1dfcdadf NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_15 git_last_commit: 374fb36 git_last_commit_date: 2022-06-28 Date/Publication: 2022-06-28 source.ver: src/contrib/BgeeDB_2.22.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/BgeeDB_2.22.3.zip mac.binary.ver: bin/macosx/contrib/4.2/BgeeDB_2.22.3.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: BgeeDB,, an R package for retrieval of curated expression datasets and for gene list enrichment tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: psygenet2r, RITAN suggestsMe: RITAN dependencyCount: 71 Package: BGmix Version: 1.56.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 33a7995efca6e35379e13db9a76859ee NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin git_url: https://git.bioconductor.org/packages/BGmix git_branch: RELEASE_3_15 git_last_commit: 06df0a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BGmix_1.56.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/BGmix_1.56.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R dependencyCount: 2 Package: bgx Version: 1.62.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: x64 MD5sum: e939bca5ee7118bb99d2175f31ba38a2 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: RELEASE_3_15 git_last_commit: b487de4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bgx_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bgx_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bgx_1.62.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R dependencyCount: 26 Package: BHC Version: 1.48.0 License: GPL-3 Archs: x64 MD5sum: ceeaeb0a9774dde7b819095fa1ff1e14 NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage git_url: https://git.bioconductor.org/packages/BHC git_branch: RELEASE_3_15 git_last_commit: baa0368 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BHC_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BHC_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BHC_1.48.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R dependencyCount: 0 Package: BicARE Version: 1.54.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: x64 MD5sum: 91246220d82f92329b18c2982072037d NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: RELEASE_3_15 git_last_commit: 47ce7e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BicARE_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BicARE_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BicARE_1.54.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R dependsOnMe: RcmdrPlugin.BiclustGUI importsMe: miRSM dependencyCount: 57 Package: BiFET Version: 1.16.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: b6e8450b688ac9655fe72978c80ff2d2 NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_15 git_last_commit: d238194 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiFET_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiFET_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiFET_1.16.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 17 Package: BiGGR Version: 1.32.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: 2064b5f11858bbaf71d301edc2420684 NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: RELEASE_3_15 git_last_commit: bf5b686 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiGGR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiGGR_1.32.0.zip vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R dependencyCount: 25 Package: bigmelon Version: 1.22.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: 2a8ee86b8fe01ade92c000fe2fb01864 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_15 git_last_commit: 94e8b0f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bigmelon_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bigmelon_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bigmelon_1.22.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 171 Package: bigPint Version: 1.12.0 Depends: R (>= 3.6.0) Imports: DelayedArray (>= 0.12.2), dplyr (>= 0.7.2), GGally (>= 1.3.2), ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc (>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly (>= 4.7.1), plyr (>= 1.8.4), RColorBrewer (>= 1.1.2), reshape (>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0), shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>= 1.3.1), SummarizedExperiment (>= 1.16.1), tidyr (>= 0.7.0), utils (>= 3.5.0) Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>= 2.14.0), edgeR (>= 3.22.2), gtools (>= 3.5.0), knitr (>= 1.13), matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>= 3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2), License: GPL-3 MD5sum: d9c8b6fd97186803577c8f1643a470f1 NeedsCompilation: no Title: Big multivariate data plotted interactively Description: Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes. biocViews: Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization Author: Lindsay Rutter [aut, cre], Dianne Cook [aut] Maintainer: Lindsay Rutter URL: https://github.com/lindsayrutter/bigPint VignetteBuilder: knitr BugReports: https://github.com/lindsayrutter/bigPint/issues git_url: https://git.bioconductor.org/packages/bigPint git_branch: RELEASE_3_15 git_last_commit: d0aeb8f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bigPint_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bigPint_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bigPint_1.12.0.tgz vignettes: vignettes/bigPint/inst/doc/bioconductor.html, vignettes/bigPint/inst/doc/manuscripts.html, vignettes/bigPint/inst/doc/summarizedExperiment.html vignetteTitles: "bigPint Vignette", "Recommended RNA-seq pipeline", "Data metrics object" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigPint/inst/doc/bioconductor.R, vignettes/bigPint/inst/doc/manuscripts.R, vignettes/bigPint/inst/doc/summarizedExperiment.R dependencyCount: 128 Package: BindingSiteFinder Version: 1.2.0 Depends: GenomicRanges, R (>= 4.1) Imports: tidyr, plyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce Suggests: testthat, BiocStyle, knitr, rmarkdown, dplyr, GenomicAlignments, ComplexHeatmap, GenomeInfoDb, forcats, scales License: Artistic-2.0 MD5sum: cbb4293bb9c2788fd986ef769c661e8c NeedsCompilation: no Title: Binding site defintion based on iCLIP data Description: Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette. biocViews: Sequencing, GeneExpression, GeneRegulation, FunctionalGenomics, Coverage, DataImport Author: Mirko Brüggemann [aut, cre] (), Kathi Zarnack [aut] () Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: RELEASE_3_15 git_last_commit: 019651b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BindingSiteFinder_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BindingSiteFinder_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BindingSiteFinder_1.2.0.tgz vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html vignetteTitles: Definition of binding sites from iCLIP signal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R dependencyCount: 87 Package: bioassayR Version: 1.34.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 955cee262545fffa0b97fd0e2cf56a25 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Daniela Cassol URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: RELEASE_3_15 git_last_commit: b6b8aca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bioassayR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioassayR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bioassayR_1.34.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 69 Package: Biobase Version: 2.56.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: x64 MD5sum: 244f712fcd84095dd97470d3b6741165 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_15 git_last_commit: 3b2dd91 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Biobase_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Biobase_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Biobase_2.56.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DEXSeq, DFP, diggit, doppelgangR, DSS, dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GEOexplorer, GeomxTools, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, INSPEcT, isobar, iterativeBMA, IVAS, lumi, macat, made4, mAPKL, massiR, MEAL, metagenomeSeq, metavizr, MethPed, methylumi, Mfuzz, MiChip, microbiomeExplorer, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, MMDiff2, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NanoStringNCTools, NanoTube, NOISeq, nondetects, normalize450K, NormqPCR, oligo, omicRexposome, OrderedList, OTUbase, pandaR, panp, pcaMethods, pdInfoBuilder, pepStat, phenoTest, PLPE, POWSC, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, RefPlus, rexposome, Ringo, Risa, Rmagpie, Rnits, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes, singleCellTK, SpeCond, SPEM, spkTools, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, VegaMC, viper, vsn, wateRmelon, webbioc, XDE, yarn, EuPathDB, affycompData, ALL, bcellViper, beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4, CLL, colonCA, CRCL18, curatedBreastData, davidTiling, diggitdata, DLBCL, dressCheck, etec16s, fabiaData, fibroEset, gaschYHS, golubEsets, GSE103322, GSE13015, GSE62944, GSVAdata, harbChIP, Hiiragi2013, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MAQCsubsetILM, MetaGxBreast, MetaGxOvarian, miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata, ProData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, pumadata, rcellminerData, RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA, tweeDEseqCountData, yeastCC, maEndToEnd, countTransformers, crmn, dGAselID, GWASbyCluster, heatmapFlex, InteRD, lmQCM, MM2Sdata, MMDvariance, propOverlap, statVisual importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, biosigner, biscuiteer, BiSeq, blima, bnem, bsseq, BubbleTree, CAFE, canceR, Cardinal, CellScore, CellTrails, CGHnormaliter, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, cogena, combi, CompoundDb, conclus, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx, crlmm, crossmeta, ctgGEM, cummeRbund, cyanoFilter, cycle, cydar, CytoML, CytoTree, DAPAR, ddCt, debCAM, deco, DEGreport, DESeq2, destiny, DExMA, diffloop, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowStats, flowUtils, flowViz, flowWorkspace, FRASER, frma, frmaTools, GAPGOM, GBScleanR, gCrisprTools, gcrma, GCSscore, genbankr, geneClassifiers, GeneExpressionSignature, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GenomicSuperSignature, GEOsubmission, gep2pep, gespeR, ggbio, girafe, GISPA, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, hermes, HTqPCR, HTSFilter, imageHTS, ImmuneSpaceR, infinityFlow, IsoformSwitchAnalyzeR, IsoGeneGUI, isomiRs, iterClust, kissDE, lapmix, LiquidAssociation, LRBaseDbi, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MeSHDbi, metaseqR2, MethylAid, methylCC, methylclock, methylumi, mfa, MiChip, microbiomeDASim, microbiomeMarker, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, MMAPPR2, mogsa, MoonlightR, MOSim, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, netZooR, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, omicsViewer, ontoProc, oposSOM, oppar, OrganismDbi, panp, phantasus, PharmacoGx, phemd, phyloseq, piano, plethy, plgem, plier, podkat, ppiStats, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, protGear, PSEA, psygenet2r, ptairMS, puma, PureCN, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, RIVER, Rmagpie, RNAinteract, rols, ropls, ROTS, RpsiXML, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, singscore, sitadela, SomaticSignatures, SpatialDecon, spkTools, SPONGE, standR, STATegRa, subSeq, synapter, TEQC, TFBSTools, timecourse, TMixClust, TnT, topdownr, ToxicoGx, tradeSeq, traviz, TTMap, twilight, uSORT, VanillaICE, variancePartition, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, wpm, xcms, Xeva, BloodCancerMultiOmics2017, ccTutorial, DeSousa2013, DExMAdata, Fletcher2013a, GSE13015, hgu133plus2CellScore, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata, pRolocdata, RNAinteractMAPK, seqc, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe, bapred, BisqueRNA, CIARA, ClassComparison, ClassDiscovery, CSCDRNA, easyDifferentialGeneCoexpression, FMradio, geneExpressionFromGEO, HiResTEC, IntegratedJM, IsoGene, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, pulseTD, ragt2ridges, RobLox, RobLoxBioC, RPPanalyzer, seAMLess, ssizeRNA, TailRank suggestsMe: AUCell, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, DART, dcanr, dearseq, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina, msa, multiClust, OSAT, PCAtools, pkgDepTools, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, sparrow, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, TimeSeriesExperiment, tkWidgets, TypeInfo, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, ccTutorial, dorothea, dyebiasexamples, HMP16SData, HMP2Data, mammaPrintData, mAPKLData, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid, CrossValidate, D4TAlink.light, distrDoc, dnet, exp2flux, GenAlgo, hexbin, HTSCluster, isatabr, mi4p, Modeler, multiclassPairs, NACHO, ordinalbayes, Patterns, pkgmaker, Platypus, propr, seeker, Seurat, sigminer, tinyarray dependencyCount: 5 Package: biobroom Version: 1.28.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, rmarkdown, SummarizedExperiment License: LGPL MD5sum: 88e8950daabf8687fc3e3ceb1210458e NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_15 git_last_commit: 1511b5d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biobroom_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biobroom_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biobroom_1.28.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 50 Package: biobtreeR Version: 1.8.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: 67ccd8a029da31f1aefc5fb7df5a60f4 NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: RELEASE_3_15 git_last_commit: 291c47f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biobtreeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biobtreeR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biobtreeR_1.8.0.tgz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 19 Package: bioCancer Version: 1.24.01 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), XML(>= 3.98) Imports: R.oo, R.methodsS3, httr, DT (>= 0.3), dplyr (>= 0.7.2), shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, AnnotationDbi, shinythemes, Biobase, geNetClassifier, org.Hs.eg.db, org.Bt.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble, GO.db Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: 93d0fcec9b8d622ff463097ec40fb61c NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_15 git_last_commit: a748d9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/bioCancer_1.24.01.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioCancer_1.24.01.zip mac.binary.ver: bin/macosx/contrib/4.2/bioCancer_1.24.01.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 227 Package: BiocCheck Version: 1.32.1 Depends: R (>= 4.2.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, jsonlite, rmarkdown, downloader, devtools (>= 1.4.1), usethis, BiocStyle Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: e5b719470b25b1c34659d54e39022cc8 NeedsCompilation: no Title: Bioconductor-specific package checks Description: BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [ctb], Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocCheck/issues git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_15 git_last_commit: bc32a68 git_last_commit_date: 2022-08-29 Date/Publication: 2022-08-30 source.ver: src/contrib/BiocCheck_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocCheck_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocCheck_1.32.1.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: AnnotationHubData suggestsMe: GEOfastq, packFinder, preciseTAD, SpectralTAD, HMP16SData, HMP2Data, scpdata dependencyCount: 37 Package: BiocDockerManager Version: 1.8.1 Depends: R (>= 4.1) Imports: httr, whisker, readr, dplyr, utils, methods, memoise Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 72832467125882fcd45413d400bcbc24 NeedsCompilation: no Title: Access Bioconductor docker images Description: Package works analogous to BiocManager but for docker images. Use the BiocDockerManager package to install and manage docker images provided by the Bioconductor project. A convenient package to install images, update images and find which Bioconductor based docker images are available. biocViews: Software, Infrastructure, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer SystemRequirements: docker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocDockerManager/issues git_url: https://git.bioconductor.org/packages/BiocDockerManager git_branch: RELEASE_3_15 git_last_commit: 0c721b5 git_last_commit_date: 2022-08-22 Date/Publication: 2022-08-25 source.ver: src/contrib/BiocDockerManager_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocDockerManager_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocDockerManager_1.8.1.tgz vignettes: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.html vignetteTitles: BiocDockerManager Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.R dependencyCount: 45 Package: BiocFileCache Version: 2.4.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, filelock, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: 2848ee1d9750e8323fb2a475084f2639 NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: RELEASE_3_15 git_last_commit: 2c00eee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocFileCache_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocFileCache_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocFileCache_2.4.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, ExperimentHub, RcwlPipelines, JASPAR2022, scATAC.Explorer, TMExplorer, csawBook, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.workflows importsMe: AMARETTO, atSNP, autonomics, BayesSpace, BiocPkgTools, biodb, biomaRt, BioPlex, BrainSABER, brendaDb, bugsigdbr, cbaf, cBioPortalData, CellBench, conclus, CTDquerier, customCMPdb, dasper, easyRNASeq, enhancerHomologSearch, EnMCB, EnrichmentBrowser, EpiTxDb, fgga, GAPGOM, GenomicScores, GenomicSuperSignature, GRaNIE, GSEABenchmarkeR, gwascat, hca, MBQN, NxtIRFcore, ODER, ontoProc, Organism.dplyr, PhIPData, psichomics, recount3, recountmethylation, regutools, rpx, sesame, SpatialExperiment, spatialHeatmap, terraTCGAdata, TFutils, tomoseqr, tximeta, UMI4Cats, UniProt.ws, waddR, org.Mxanthus.db, PANTHER.db, NxtIRFdata, SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations suggestsMe: AnnotationForge, bambu, BiocOncoTK, BiocSet, EpiCompare, fastreeR, FLAMES, HiCDCPlus, HumanTranscriptomeCompendium, MethReg, Nebulosa, progeny, qsvaR, seqsetvis, structToolbox, TCGAutils, TimeSeriesExperiment, TREG, zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData, msigdb, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, SingleRBook dependencyCount: 44 Package: BiocGenerics Version: 0.42.0 Depends: R (>= 4.0.0), methods, utils, graphics, stats Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 7e10e7ca16bcd6d1dafe2fc64ee567a9 NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines many S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: RELEASE_3_15 git_last_commit: 3582d47 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocGenerics_0.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocGenerics_0.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocGenerics_0.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, clusterExperiment, codelink, consensusDE, consensusSeekeR, copynumber, CoreGx, CRISPRseek, cummeRbund, DelayedArray, ensembldb, ensemblVEP, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, ggbio, girafe, graph, GSEABase, GUIDEseq, HelloRanges, interactiveDisplay, interactiveDisplayBase, IRanges, MBASED, MIGSA, MineICA, minfi, MLInterfaces, MotifDb, mpra, MSnbase, multtest, NADfinder, ngsReports, oligo, OrganismDbi, plethy, plyranges, PoTRA, profileplyr, PSICQUIC, PWMEnrich, RareVariantVis, REDseq, Repitools, RnBeads, RPA, rsbml, S4Vectors, shinyMethyl, ShortRead, simplifyEnrichment, soGGi, spqn, StructuralVariantAnnotation, SummarizedBenchmark, svaNUMT, svaRetro, TEQC, tigre, topdownr, topGO, UNDO, UniProt.ws, updateObject, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss, ChAMPdata, liftOver, rsolr importsMe: a4Preproc, affycoretools, affylmGUI, AllelicImbalance, AneuFinder, annmap, annotate, AnnotationHubData, ArrayExpressHTS, ASpli, atena, AUCell, autonomics, bambu, bamsignals, BASiCS, batchelor, beachmat, bigmelon, biocGraph, BiocIO, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, blima, breakpointR, BrowserViz, BSgenome, BubbleTree, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, celaref, CellaRepertorium, CellBench, cellHTS2, CellMixS, CellTrails, cghMCR, ChemmineOB, ChemmineR, ChIC, chipenrich, ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, chromstaR, chromVAR, cicero, clusterSeq, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, cola, compEpiTools, CompoundDb, contiBAIT, crisprBase, crisprBowtie, crisprBwa, crisprScore, crlmm, crossmeta, csaw, ctgGEM, cummeRbund, cydar, dada2, dagLogo, DAMEfinder, ddCt, decompTumor2Sig, deconvR, DEGreport, DelayedDataFrame, derfinder, DEScan2, DESeq2, destiny, DEWSeq, DEXSeq, diffcoexp, diffHic, DirichletMultinomial, DiscoRhythm, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisaR, enhancerHomologSearch, enrichTF, epialleleR, epigenomix, epimutacions, epistack, EpiTxDb, epivizrChart, epivizrStandalone, erma, esATAC, FamAgg, fastseg, ffpe, FindIT2, FLAMES, flowBin, flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats, flowWorkspace, fmcsR, FRASER, frma, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, geneClassifiers, genefilter, GENESIS, GenomicAlignments, GenomicInteractions, GenomicTuples, genotypeeval, GenVisR, GeomxTools, glmGamPoi, gmapR, gmoviz, GOTHiC, gpuMagic, Gviz, HDF5Array, heatmaps, hermes, HiLDA, hiReadsProcessor, hopach, HTSeqGenie, icetea, igvR, IHW, IMAS, infercnv, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, IsoformSwitchAnalyzeR, isomiRs, IVAS, KCsmart, ldblock, LinTInd, lisaClust, LOLA, maser, MAST, matter, MEAL, meshr, MetaboAnnotation, metaMS, metaseqR2, methInheritSim, MethylAid, methylPipe, methylumi, mia, miaViz, microbiomeMarker, miloR, mimager, MinimumDistance, MIRA, MiRaGE, missMethyl, MMAPPR2, Modstrings, mogsa, monaLisa, monocle, Motif2Site, motifbreakR, msa, MSnID, MultiAssayExperiment, multicrispr, MultiDataSet, multiMiR, mumosa, MutationalPatterns, mzR, NanoStringNCTools, nearBynding, npGSEA, nucleR, NxtIRFcore, ODER, oligoClasses, OmicsLonDA, openPrimeR, ORFik, OUTRIDER, parglms, pcaMethods, PDATK, pdInfoBuilder, PharmacoGx, phemd, PhIPData, PhosR, phyloseq, Pi, piano, PING, podkat, pram, primirTSS, proDA, profileScoreDist, pRoloc, pRolocGUI, ProteoDisco, PSMatch, PureCN, pwOmics, QFeatures, qPLEXanalyzer, qsea, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, RaggedExperiment, ramr, ramwas, RCAS, RcisTarget, RCy3, RCyjs, recoup, REMP, ReportingTools, RGMQL, RGSEA, RiboCrypt, RiboProfiling, ribosomeProfilingQC, Ringo, RJMCMCNucleosomes, rnaEditr, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, rols, Rqc, rqubic, Rsamtools, rsbml, rScudo, RTCGAToolbox, rtracklayer, SC3, SCArray, scater, scDblFinder, scmap, SCnorm, SCOPE, scPipe, scran, scruff, scuttle, SeqVarTools, sevenC, SGSeq, SharedObject, signatureSearch, signeR, single, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, SNPhood, snpStats, sparrow, SpatialExperiment, spatzie, Spectra, spicyR, splatter, SplicingGraphs, SQLDataFrame, sRACIPE, sscu, STAN, standR, strandCheckR, Streamer, Structstrings, SummarizedExperiment, SynMut, systemPipeR, TAPseq, target, TarSeqQC, TBSignatureProfiler, TCGAutils, TCseq, TFBSTools, TitanCNA, ToxicoGx, trackViewer, transcriptR, transite, TransView, TreeSummarizedExperiment, tRNA, tRNAdbImport, tRNAscanImport, TVTB, txcutr, Ularcirc, UMI4Cats, unifiedWMWqPCR, universalmotif, uSORT, VariantTools, velociraptor, wavClusteR, weitrix, xcms, XDE, XVector, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, curatedCRCData, curatedOvarianData, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, scRNAseq, SingleCellMultiModal, spatialLIBD, systemPipeRdata, TENxBUSData, VariantToolsData, ExpHunterSuite, GeoMxWorkflows, crispRdesignR, DCLEAR, EEMDlstm, geno2proteo, oncoPredict, PACVr, pagoo, pathwayTMB, Platypus, RobLoxBioC, SCRIP, scTEP, Signac, spectralAnalysis, toxpiR, TSdeeplearning, utr.annotation, xQTLbiolinks suggestsMe: acde, aggregateBioVar, AIMS, ASSET, ASURAT, BaalChIP, baySeq, BDMMAcorrect, bigmelon, bigPint, BiocCheck, BiocParallel, BiocStyle, biocViews, BioMM, biosigner, BiRewire, BLMA, BloodGen3Module, bnem, borealis, BUScorrect, BUSseq, CAFE, CAMERA, CancerSubtypes, CAnD, CausalR, ccrepe, cellmigRation, CexoR, ChIPanalyser, ChIPXpress, CHRONOS, CINdex, cleanUpdTSeq, clipper, clonotypeR, clustComp, CNORfeeder, CNORfuzzy, coMET, consensus, cosmiq, COSNet, cpvSNP, CytoTree, DEsubs, DExMA, DMRcaller, DMRcate, EnhancedVolcano, ENmix, epiNEM, EventPointer, fCCAC, fcScan, fgga, FGNet, flowCL, flowCut, flowTime, fmrs, GateFinder, gCrisprTools, gdsfmt, GEM, GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster, geNetClassifier, genomation, GEOquery, GMRP, GOstats, GraphPAC, GreyListChIP, GSVA, GWASTools, h5vc, Harman, hiAnnotator, HiCDCPlus, hierGWAS, HIREewas, HPiP, hypergraph, iCARE, iClusterPlus, illuminaio, immunotation, INPower, IPO, kebabs, KEGGREST, LACE, MAGAR, mAPKL, massiR, MatrixQCvis, MatrixRider, MBttest, mCSEA, Mergeomics, Metab, MetaboSignal, metagene, metagene2, metagenomeSeq, MetCirc, methylCC, methylInheritance, MetNet, microbiome, miRBaseConverter, miRcomp, mirIntegrator, mnem, mosbi, motifStack, multiClust, MultiMed, multiOmicsViz, MungeSumstats, MWASTools, NBSplice, ncRNAtools, nempi, NetSAM, nondetects, nucleoSim, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, PathNet, pathview, PCAtools, pepXMLTab, PhenStat, powerTCR, proBAMr, proFIA, pwrEWAS, qpgraph, quantro, QuartPAC, RBGL, rBiopaxParser, Rcade, rcellminer, rCGH, Rcpi, REBET, rfaRm, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sarks, scDataviz, scmeth, scry, segmentSeq, SeqArray, seqPattern, seqTools, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, singleCellTK, SingleR, slingshot, SNPRelate, sojourner, SpacePAC, sparseDOSSA, SparseSignatures, spatialHeatmap, specL, STATegRa, STRINGdb, systemPipeTools, TCC, TFEA.ChIP, TIN, transcriptogramer, TraRe, traseR, TreeAndLeaf, trena, tripr, TRONCO, Uniquorn, variancePartition, VERSO, xcore, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, healthyControlsPresenceChecker, microRNAome, MIGSAdata, pwrEWAS.data, RegParallel, sesameData, xcoredata, adjclust, aroma.affymetrix, asteRisk, gkmSVM, MetaIntegrator, NutrienTrackeR, openSkies, pagoda2, polyRAD, Rediscover, Seurat dependencyCount: 4 Package: biocGraph Version: 1.58.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: f24ed82eca45c0a93c29b74a72b46e15 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: RELEASE_3_15 git_last_commit: 2916b2a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biocGraph_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocGraph_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biocGraph_1.58.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: EnrichmentBrowser dependencyCount: 54 Package: BiocIO Version: 1.6.0 Depends: R (>= 4.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 8668bb4b5aa3b14295ccba8cd3d4dc33 NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: RELEASE_3_15 git_last_commit: 60c8aa1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocIO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocIO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocIO_1.6.0.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: HelloRanges, LoomExperiment importsMe: BiocSet, extraChIPs, GenomicFeatures, rtracklayer dependencyCount: 8 Package: BiocNeighbors Version: 1.14.0 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: x64 MD5sum: bd91cad15b0bf210f91094a866c2c3bc NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: RELEASE_3_15 git_last_commit: 670a1bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocNeighbors_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocNeighbors_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocNeighbors_1.14.0.tgz vignettes: vignettes/BiocNeighbors/inst/doc/approx.html, vignettes/BiocNeighbors/inst/doc/exact.html, vignettes/BiocNeighbors/inst/doc/range.html vignetteTitles: 2. Detecting approximate nearest neighbors, 1. Detecting exact nearest neighbors, 3. Detecting neighbors within range hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/approx.R, vignettes/BiocNeighbors/inst/doc/exact.R, vignettes/BiocNeighbors/inst/doc/range.R dependsOnMe: OSCA.advanced, OSCA.workflows importsMe: batchelor, bluster, CellMixS, cydar, CytoTree, imcRtools, miloR, mumosa, scater, scDblFinder, SingleR suggestsMe: TrajectoryUtils, TSCAN, SingleRBook dependencyCount: 22 Package: BiocOncoTK Version: 1.16.0 Depends: R (>= 3.6.0), methods, utils Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr, rjson, dplyr, magrittr, grid, DT, GenomicRanges, IRanges, ggplot2, SummarizedExperiment, DBI, GenomicFeatures, curatedTCGAData, scales, ggpubr, plyr, car, graph, Rgraphviz Suggests: knitr, dbplyr, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, GenomeInfoDb, restfulSE (>= 1.3.7), BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, Biobase, TxDb.Hsapiens.UCSC.hg18.knownGene, reshape2, testthat, AnnotationDbi, FDb.InfiniumMethylation.hg19, EnsDb.Hsapiens.v75, rmarkdown, rhdf5client License: Artistic-2.0 MD5sum: 2470a95cc1d7382235152cc75b2ac29d NeedsCompilation: no Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_15 git_last_commit: fef8b96 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocOncoTK_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocOncoTK_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocOncoTK_1.16.0.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html, vignettes/BiocOncoTK/inst/doc/curatedMSIData.html, vignettes/BiocOncoTK/inst/doc/maptcga.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor, curatedMSIData overview, "Mapping TCGA tumor codes to NCIT" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R, vignettes/BiocOncoTK/inst/doc/curatedMSIData.R, vignettes/BiocOncoTK/inst/doc/maptcga.R dependencyCount: 204 Package: BioCor Version: 1.20.0 Depends: R (>= 3.4.0) Imports: BiocParallel, GSEABase, Matrix, methods Suggests: airway, BiocStyle, boot, DESeq2, GOSemSim, Hmisc, knitr (>= 1.35), org.Hs.eg.db, reactome.db, rmarkdown, spelling, targetscan.Hs.eg.db, testthat, WGCNA License: MIT + file LICENSE MD5sum: 140f241a4619f91b7516e34950aba3a3 NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (), Pau Sancho-Bru [ths] (), Juan José Salvatella Lozano [ths] () Maintainer: Lluís Revilla Sancho URL: https://bioconductor.org/packages/BioCor, https://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_15 git_last_commit: a77530b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioCor_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioCor_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioCor_1.20.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 62 Package: BiocParallel Version: 1.30.4 Depends: methods, R (>= 3.5.0) Imports: stats, utils, futile.logger, parallel, snow, codetools LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: x64 MD5sum: 1ea41791e9c5318fccec03f5dff4cc77 NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Martin Morgan [aut, cre], Jiefei Wang [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb] Maintainer: Martin Morgan URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_15 git_last_commit: 3f8bbc2 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-11 source.ver: src/contrib/BiocParallel_1.30.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocParallel_1.30.4.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocParallel_1.30.4.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf, vignettes/BiocParallel/inst/doc/Random_Numbers.pdf vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel, 4. Random Numbers in BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R, vignettes/BiocParallel/inst/doc/Random_Numbers.R dependsOnMe: bacon, BEclear, Cardinal, ChIPQC, ClassifyR, clusterSeq, consensusSeekeR, CopywriteR, deco, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, extraChIPs, FEAST, FRASER, GenomicFiles, hiReadsProcessor, INSPEcT, iPath, matter, MBASED, metagene, metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, Rqc, ShortRead, SigCheck, Spectra, STROMA4, SummarizedBenchmark, sva, variancePartition, xcms, sequencing, OSCA.advanced, OSCA.workflows importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR, amplican, ASICS, ASpediaFI, atena, atSNP, bambu, BANDITS, bandle, BASiCS, batchelor, bayNorm, beer, benchdamic, BiocNeighbors, BioCor, BiocSingular, BioMM, BioNERO, BioNetStat, biotmle, biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, CBEA, cellbaseR, CellBench, CelliD, CellMixS, censcyt, Cepo, ChIPexoQual, ChromSCape, chromswitch, chromVAR, CNVMetrics, CNVRanger, CoGAPS, comapr, coMethDMR, condiments, consensusDE, contiBAIT, CoreGx, coseq, cpvSNP, CrispRVariants, csaw, cydar, CytoGLMM, cytoKernel, cytomapper, dasper, dcGSA, debCAM, DEComplexDisease, DepInfeR, derfinder, DEScan2, DESeq2, DEsingle, DiffBind, Dino, dmrseq, DOSE, DRIMSeq, DropletUtils, Dune, easier, easyRNASeq, EMDomics, enhancerHomologSearch, epimutacions, erma, ERSSA, escape, EWCE, fgsea, FindIT2, flowcatchR, flowSpecs, GDCRNATools, GENESIS, GenomicAlignments, genotypeeval, gmapR, GRaNIE, gscreend, GSEABenchmarkeR, GSVA, GUIDEseq, h5vc, HiCBricks, HiCcompare, HTSeqGenie, HTSFilter, iasva, icetea, ideal, IMAS, imcRtools, IntEREst, IONiseR, IPO, ISAnalytics, KinSwingR, LineagePulse, lisaClust, loci2path, LowMACA, LRcell, Macarron, MACPET, mbkmeans, MCbiclust, MetaboAnnotation, metabomxtr, metaseqR2, MethylAid, methylGSA, methylInheritance, methylscaper, MetNet, mia, miaViz, microbiomeMarker, MIGSA, miloR, minfi, mixOmics, MMAPPR2, MOGAMUN, monaLisa, motifbreakR, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MSnbase, msqrob2, MSstatsSampleSize, multiHiCcompare, mumosa, muscat, NBAMSeq, NBSplice, nnSVG, NPARC, NxtIRFcore, OmicsLonDA, ORFik, OVESEG, PAIRADISE, PCAtools, PDATK, pengls, PharmacoGx, pipeComp, pram, PrecisionTrialDrawer, proActiv, proFIA, profileplyr, ProteoDisco, PSMatch, qpgraph, qsea, QuasR, RadioGx, Rcwl, recount, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, Rsamtools, RUVcorr, satuRn, scanMiR, scanMiRApp, scater, scClassify, scDblFinder, scDD, scde, SCFA, scHOT, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, scruff, scShapes, scTHI, scuttle, seqArchR, sesame, SEtools, sigFeature, signatureSearch, singleCellTK, SingleR, singscore, SNPhood, soGGi, sparrow, spatialHeatmap, SpectralTAD, spicyR, splatter, SplicingGraphs, srnadiff, sSNAPPY, STdeconvolve, TAPseq, TarSeqQC, TBSignatureProfiler, ternarynet, TFBSTools, TMixClust, ToxicoGx, TPP2D, tradeSeq, TraRe, TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell, VariantFiltering, VariantTools, velociraptor, waddR, weitrix, zinbwave, IHWpaper, ExpHunterSuite, DCLEAR, DysPIA, enviGCMS, minSNPs suggestsMe: beachmat, DelayedArray, DIAlignR, EpiCompare, GenomicDataCommons, glmGamPoi, HDF5Array, netSmooth, omicsPrint, PureCN, randRotation, RcisTarget, rebook, scGPS, SeqArray, TFutils, TileDBArray, TrajectoryUtils, trena, TSCAN, universalmotif, xcore, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, SingleRBook, conos, Corbi, digitalDLSorteR, pagoda2, phase1RMD, survBootOutliers, wrTopDownFrag dependencyCount: 11 Package: BiocPkgTools Version: 1.14.1 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, magrittr, methods, rlang, tidyselect, stringr, rvest, dplyr, xml2, readr, httr, htmltools, DT, tools, utils, igraph, tidyr, jsonlite, gh, RBGL, graph Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, SnowballC, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: b4a7a92292dfbb571f49ebdc99c679d5 NeedsCompilation: no Title: Collection of simple tools for learning about Bioc Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [ctb], Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: RELEASE_3_15 git_last_commit: 194b8cc git_last_commit_date: 2022-08-16 Date/Publication: 2022-08-16 source.ver: src/contrib/BiocPkgTools_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocPkgTools_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocPkgTools_1.14.1.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R dependencyCount: 91 Package: BiocSet Version: 1.10.0 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 30a15f3846e7655cad9983a4fc0d3004 NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet. biocViews: GeneExpression, GO, KEGG, Software Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin Rue-Albrecht [ctb], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSet git_branch: RELEASE_3_15 git_last_commit: 333920f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocSet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocSet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocSet_1.10.0.tgz vignettes: vignettes/BiocSet/inst/doc/BiocSet.html vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R dependsOnMe: RegEnrich importsMe: CBEA, sparrow suggestsMe: dearseq dependencyCount: 62 Package: BiocSingular Version: 1.12.0 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat LinkingTo: Rcpp, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix License: GPL-3 Archs: x64 MD5sum: 6b820bb0eb5612cd44be65ab08ba2627 NeedsCompilation: yes Title: Singular Value Decomposition for Bioconductor Packages Description: Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: RELEASE_3_15 git_last_commit: 7d1b8f4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocSingular_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocSingular_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocSingular_1.12.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R dependsOnMe: compartmap, OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: batchelor, BayesSpace, clusterExperiment, DelayedTensor, Dino, GSVA, miloR, mumosa, NanoMethViz, NewWave, PCAtools, scater, scDblFinder, scMerge, scran, scry, SingleR, velociraptor suggestsMe: ResidualMatrix, ScaledMatrix, spatialHeatmap, splatter, HCAData dependencyCount: 29 Package: BiocSklearn Version: 1.18.2 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment Imports: basilisk Suggests: testthat, restfulSE, HDF5Array, BiocStyle, rmarkdown, knitr License: Artistic-2.0 MD5sum: b47a15310e9c8297fb6f88486e24cec6 NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey [cre, aut] Maintainer: Vince Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: RELEASE_3_15 git_last_commit: e8e1487 git_last_commit_date: 2022-06-01 Date/Publication: 2022-06-02 source.ver: src/contrib/BiocSklearn_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocSklearn_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocSklearn_1.18.2.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 39 Package: BiocStyle Version: 2.24.0 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: 0feb2de681a9adc5902209fdb597300d NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś [aut] (), Mike Smith [ctb] (), Martin Morgan [ctb], Wolfgang Huber [ctb], Bioconductor Package [cre] Maintainer: Bioconductor Package URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: RELEASE_3_15 git_last_commit: 53095b5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocStyle_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocStyle_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocStyle_2.24.0.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.R, vignettes/BiocStyle/inst/doc/LatexStyle2.R dependsOnMe: netresponse, Pigengene, RPA, sangeranalyseR, org.Mxanthus.db, curatedBreastData, cytofWorkflow, methylationArrayAnalysis, rnaseqGene, RnaSeqGeneEdgeRQL, OSCA importsMe: abseqR, ASpli, bandle, BiocWorkflowTools, BPRMeth, BubbleTree, chimeraviz, COMPASS, deco, DiscoRhythm, geneXtendeR, Melissa, meshr, MSnID, PathoStat, PhyloProfile, rebook, regionReport, Rmmquant, Rqc, scTensor, scTGIF, srnadiff, FieldEffectCrc, PhyloProfileData, GeoMxWorkflows, simpleSingleCell, EZtune, scImmuneGraph, SNPassoc suggestsMe: ACE, ADAMgui, ADImpute, AffiXcan, affycoretools, aggregateBioVar, ALDEx2, alevinQC, AllelicImbalance, AMOUNTAIN, amplican, AneuFinder, animalcules, AnnotationDbi, AnnotationFilter, AnnotationForge, AnnotationHub, AnnotationHubData, annotationTools, annotatr, AnVIL, AnVILBilling, AnVILPublish, APAlyzer, APL, arrayQualityMetrics, artMS, ASGSCA, ASICS, AssessORF, ASSIGN, ATACseqQC, atena, atSNP, AUCell, awst, BaalChIP, bacon, bamsignals, BANDITS, banocc, barcodetrackR, basecallQC, BASiCS, basilisk, basilisk.utils, batchelor, bayNorm, baySeq, beachmat, beadarray, BeadDataPackR, BEARscc, BEclear, beer, benchdamic, BgeeDB, bigmelon, BindingSiteFinder, bioassayR, biobtreeR, bioCancer, BiocCheck, BiocDockerManager, BiocFileCache, BiocIO, BiocNeighbors, BiocOncoTK, BioCor, BiocParallel, BiocPkgTools, BiocSet, BiocSingular, BiocSklearn, biocthis, biodb, biodbChebi, biodbExpasy, biodbHmdb, biodbKegg, biodbLipidmaps, biodbMirbase, biodbNcbi, biodbNci, biodbUniprot, biomaRt, biomformat, BioMM, BioNERO, BioPlex, biosigner, biotmle, BitSeq, blacksheepr, blima, bluster, bnbc, BOBaFIT, borealis, brainflowprobes, BrainSABER, branchpointer, breakpointR, brendaDb, BRGenomics, BridgeDbR, BrowserViz, bsseq, bugsigdbr, BUMHMM, BumpyMatrix, BUScorrect, BUSpaRse, BUSseq, CAFE, CAGEfightR, cageminer, CAGEr, canceR, CancerInSilico, CAnD, Cardinal, CARNIVAL, CATALYST, cbaf, CBEA, cBioPortalData, cbpManager, ccfindR, ccrepe, celda, CellaRepertorium, CellBarcode, cellbaseR, CellBench, CelliD, cellity, CellMapper, CellMixS, cellTree, cellxgenedp, censcyt, Cepo, CexoR, cfDNAPro, ChemmineOB, ChemmineR, Chicago, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromstaR, chromswitch, CIMICE, CiteFuse, ClassifyR, cleanUpdTSeq, cleaver, clipper, cliProfiler, clusterExperiment, clusterSeq, ClusterSignificance, clustifyr, cmapR, CNEr, CNVMetrics, CNVRanger, COCOA, CoGAPS, cogeqc, Cogito, comapr, coMET, coMethDMR, compcodeR, CompoundDb, conclus, CONFESS, consensusOV, consensusSeekeR, CONSTANd, contiBAIT, conumee, CopyNumberPlots, CopywriteR, coRdon, CoRegNet, CoreGx, corral, coseq, cosmiq, COTAN, covRNA, cpvSNP, crisprBowtie, crisprBwa, crisprScore, CRISPRseek, CrispRVariants, csaw, csdR, CSSQ, CTDquerier, ctgGEM, ctsGE, customCMPdb, cydar, CyTOFpower, CytoGLMM, cytoKernel, cytomapper, CytoTree, dada2, dagLogo, DAMEfinder, DaMiRseq, DAPAR, dasper, dcanr, dce, ddPCRclust, debCAM, decompTumor2Sig, decontam, deconvR, decoupleR, DEFormats, DEGreport, DelayedArray, DelayedMatrixStats, DelayedRandomArray, DelayedTensor, densvis, DEP, DepecheR, derfinder, derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DExMA, DEXSeq, DIAlignR, DiffBind, diffcyt, DifferentialRegulation, diffuStats, diffUTR, Dino, dir.expiry, discordant, dittoSeq, DMRScan, dmrseq, DNABarcodeCompatibility, DNABarcodes, doppelgangR, Doscheda, doseR, drawProteins, DRIMSeq, DropletUtils, drugTargetInteractions, DSS, dStruct, dupRadar, easier, easyreporting, easyRNASeq, EBImage, EDASeq, EGSEA, eiR, eisaR, ELMER, EmpiricalBrownsMethod, EnrichmentBrowser, ensembldb, EpiCompare, EpiDISH, epigraHMM, epimutacions, epistack, EpiTxDb, epivizr, epivizrChart, epivizrData, epivizrServer, epivizrStandalone, erma, ERSSA, escape, evaluomeR, EventPointer, EWCE, ExperimentHub, ExperimentHubData, ExperimentSubset, ExploreModelMatrix, extraChIPs, FamAgg, famat, FastqCleaner, fastreeR, fCCAC, fCI, fcScan, FEAST, FELLA, FilterFFPE, FindIT2, FLAMES, flowAI, flowcatchR, flowGraph, flowMap, FlowSOM, flowSpecs, fmcsR, fobitools, FRASER, GA4GHclient, GA4GHshiny, GARS, GBScleanR, gcapc, GCSConnection, GCSFilesystem, GDSArray, genbankr, GeneAccord, GeneExpressionSignature, genefilter, genefu, GeneNetworkBuilder, GeneOverlap, geneplast, GENESIS, GeneStructureTools, GeneTonic, GenomeInfoDb, GenomicAlignments, GenomicDataCommons, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicRanges, GenomicScores, GenomicSuperSignature, GenomicTuples, GenVisR, ggbio, ggmanh, ggmsa, GGPA, ggspavis, GladiaTOX, Glimma, glmGamPoi, glmSparseNet, gmoviz, GMRP, GOexpress, GOfuncR, GOpro, goSTAG, gpart, gpuMagic, GRaNIE, granulator, graper, graphite, GreyListChIP, GRmetrics, groHMM, GSAR, GSEABase, GSEABenchmarkeR, GSEAmining, GSgalgoR, GSVA, GUIDEseq, Gviz, GWAS.BAYES, gwascat, gwasurvivr, GWENA, Harman, hca, HelloRanges, hermes, HGC, HiCBricks, HiLDA, hipathia, HIREewas, HiTC, HPAanalyze, hpar, HPiP, HTSFilter, HumanTranscriptomeCompendium, ideal, iGC, IgGeneUsage, igvR, IHW, illuminaio, imageHTS, IMAS, imcRtools, immunoClust, immunotation, infercnv, InPAS, INSPEcT, InTAD, InteractionSet, InterCellar, InterMineR, IONiseR, iPath, IRanges, ISAnalytics, iSEE, iSEEu, isomiRs, IVAS, karyoploteR, kissDE, LACE, ldblock, lefser, lineagespot, LinkHD, Linnorm, lipidr, lisaClust, loci2path, LOLA, LoomExperiment, LowMACA, lpsymphony, LRBaseDbi, LRcell, m6Aboost, Macarron, MACPET, MACSr, made4, MAGeCKFlute, MAI, mAPKL, marr, maser, MassSpecWavelet, MAST, MatrixQCvis, MatrixRider, matter, MBASED, MBECS, mbkmeans, MBttest, MCbiclust, mCSEA, MEAL, MEAT, MEDIPS, megadepth, messina, metabCombiner, MetaboAnnotation, MetaboCoreUtils, metabolomicsWorkbenchR, MetaboSignal, metagene, metagene2, metapod, metavizr, methimpute, methInheritSim, MethPed, MethReg, MethylAid, methylCC, methylclock, methylInheritance, MethylMix, methylSig, MetNet, mfa, mia, miaSim, miaViz, microbiome, microbiomeMarker, MIGSA, miloR, mimager, minfi, miQC, MIRA, miRcomp, miRmine, miRSM, miRspongeR, mirTarRnaSeq, missMethyl, missRows, mistyR, mixOmics, MMAPPR2, MMDiff2, MMUPHin, moanin, MobilityTransformR, MODA, Modstrings, MOFA2, mogsa, MOMA, monaLisa, MoonlightR, mosbi, MOSim, Motif2Site, motifbreakR, MotifDb, motifStack, MouseFM, mpra, MQmetrics, MSA2dist, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsCoreUtils, MsFeatures, msImpute, MSnbase, MSPrep, msPurity, msqrob2, MSstats, MSstatsLiP, MSstatsLOBD, MSstatsPTM, MSstatsSampleSize, MSstatsTMT, MuData, MultiAssayExperiment, MultiBaC, multicrispr, MultiDataSet, multiGSEA, multiHiCcompare, multiMiR, multiSight, mumosa, MungeSumstats, muscat, musicatk, MutationalPatterns, MWASTools, mygene, myvariant, mzR, NADfinder, NanoStringDiff, NanoStringQCPro, NBSplice, ncGTW, ncRNAtools, ndexr, Nebulosa, netboxr, netDx, nethet, netOmics, NetPathMiner, netprioR, netSmooth, NeuCA, NewWave, ngsReports, nnSVG, nondetects, NormalyzerDE, normr, NPARC, npGSEA, nucleoSim, nucleR, ODER, oligo, omicade4, omicRexposome, omicsPrint, omicsViewer, Omixer, OmnipathR, OncoScore, OncoSimulR, ontoProc, optimalFlow, OPWeight, ORFhunteR, ORFik, Organism.dplyr, orthogene, Oscope, OUTRIDER, OVESEG, PAA, packFinder, padma, PAIRADISE, pairkat, PanomiR, pareg, parglms, parody, Path2PPI, paxtoolsr, pcaExplorer, PCAN, PDATK, PeacoQC, peakPantheR, PepsNMR, phantasus, phenopath, philr, PhIPData, PhosR, phyloseq, Pi, piano, pipeComp, plethy, plotGrouper, plyranges, pmp, pogos, POMA, PoTRA, powerTCR, POWSC, ppcseq, pqsfinder, pram, preciseTAD, PrecisionTrialDrawer, PrInCE, profileplyr, profileScoreDist, progeny, projectR, pRoloc, pRolocGUI, Prostar, ProteoDisco, ProteoMM, PSEA, PSMatch, ptairMS, PureCN, PWMEnrich, qcmetrics, QDNAseq, QFeatures, qmtools, qpgraph, qsea, qsmooth, QSutils, qsvaR, Qtlizer, quantiseqr, quantro, QuasR, R3CPET, RadioGx, RaggedExperiment, rain, ramwas, RandomWalkRestartMH, randRotation, RAREsim, rawrr, Rbowtie, Rbwa, Rcade, rcellminer, rCGH, RcisTarget, Rcwl, RcwlPipelines, RCX, RCy3, RCyjs, ReactomePA, recount, recount3, recountmethylation, recoup, RedeR, regioneR, regsplice, regutools, ReQON, ResidualMatrix, restfulSE, rexposome, rfaRm, Rfastp, rfPred, RGMQL, rgoslin, RGraph2js, RGSEA, rhdf5, rhdf5client, rhdf5filters, Rhdf5lib, Rhisat2, Rhtslib, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, rifi, RIVER, RJMCMCNucleosomes, RLSeq, rmspc, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, rnaseqcomp, RnaSeqSampleSize, Rnits, ROCpAI, rols, ropls, rprimer, rpx, rqt, rrvgo, Rsamtools, rScudo, rsemmed, rSWeeP, RTCGAToolbox, RTN, RTNduals, RTNsurvival, Rtpca, rTRM, RUVSeq, RVS, rWikiPathways, S4Vectors, sampleClassifier, sangerseqR, satuRn, ScaledMatrix, scanMiR, scanMiRApp, scater, scCB2, scClassify, sccomp, scDblFinder, scDD, scds, scFeatureFilter, scMerge, SCnorm, scone, scoreInvHap, scp, scPCA, scran, scReClassify, scRepertoire, scruff, scTreeViz, scuttle, sechm, segmentSeq, selectKSigs, seqArchR, seqCAT, SeqGate, seqLogo, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenC, SGSeq, SharedObject, shinyMethyl, ShortRead, SIAMCAT, SigCheck, SigFuge, signatureSearch, SigsPack, SIMD, SimFFPE, similaRpeak, SIMLR, sincell, single, SingleCellExperiment, singleCellTK, SingleR, sitePath, slalom, slingshot, SMAD, snapcount, snifter, SNPediaR, SNPhood, soGGi, sojourner, SOMNiBUS, sparrow, sparseDOSSA, sparseMatrixStats, sparsenetgls, SparseSignatures, SpatialCPie, spatialDE, SpatialExperiment, spatialHeatmap, specL, Spectra, SpectralTAD, spicyR, SpidermiR, splatter, SPLINTER, splots, SPOTlight, spqn, SPsimSeq, sRACIPE, srnadiff, sSNAPPY, stageR, STAN, StarBioTrek, STATegRa, statTarget, STdeconvolve, strandCheckR, struct, Structstrings, structToolbox, SubCellBarCode, SummarizedBenchmark, SummarizedExperiment, sva, svaRetro, swfdr, switchde, synapsis, synapter, SynExtend, systemPipeR, systemPipeShiny, systemPipeTools, TADCompare, tanggle, TAPseq, TargetDecoy, TargetSearch, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TEKRABber, terraTCGAdata, TFARM, TFBSTools, TFHAZ, TFutils, tidybulk, tidySingleCellExperiment, tidySummarizedExperiment, tigre, TileDBArray, timeOmics, TMixClust, TOAST, tomoda, tomoseqr, topconfects, topdownr, ToxicoGx, TPP, tracktables, trackViewer, TrajectoryUtils, transcriptogramer, transcriptR, transomics2cytoscape, TraRe, Travel, TreeAndLeaf, treekoR, TreeSummarizedExperiment, TREG, Trendy, tricycle, tripr, tRNA, tRNAdbImport, tRNAscanImport, TRONCO, TTMap, TurboNorm, TVTB, twoddpcr, txcutr, UCell, Ularcirc, UMI4Cats, uncoverappLib, UniProt.ws, updateObject, variancePartition, VariantAnnotation, VariantFiltering, VCFArray, velociraptor, VERSO, vidger, ViSEAGO, vissE, vsn, wateRmelon, wavClusteR, weitrix, wpm, xcms, xcore, Xeva, XNAString, yamss, YAPSA, zellkonverter, zinbwave, AHEnsDbs, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, CTCF, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, excluderanges, geneplast.data, GenomicState, hpAnnot, JASPAR2022, rat2302frmavecs, synaptome.data, UCSCRepeatMasker, ASICSdata, AssessORFData, benchmarkfdrData2019, BioImageDbs, blimaTestingData, BloodCancerMultiOmics2017, bodymapRat, CardinalWorkflows, celldex, CellMapperData, chipenrich.data, ChIPexoQualExample, chipseqDBData, CLLmethylation, clustifyrdatahub, CopyhelpeR, COSMIC.67, crisprScoreData, curatedBladderData, curatedCRCData, curatedMetagenomicData, curatedOvarianData, curatedTBData, curatedTCGAData, depmap, derfinderData, DExMAdata, DmelSGI, dorothea, DropletTestFiles, DuoClustering2018, easierData, ELMER.data, emtdata, epimutacionsData, ewceData, furrowSeg, GenomicDistributionsData, GeuvadisTranscriptExpr, GSE103322, GSE13015, GSE159526, GSE62944, HarmanData, HCAData, HD2013SGI, HDCytoData, healthyControlsPresenceChecker, HelloRangesData, HighlyReplicatedRNASeq, Hiiragi2013, HMP16SData, HMP2Data, HumanAffyData, IHWpaper, imcdatasets, LRcellTypeMarkers, mCSEAdata, MetaGxOvarian, MetaGxPancreas, MethylAidData, MethylSeqData, microbiomeDataSets, minionSummaryData, MMAPPR2data, MouseGastrulationData, MouseThymusAgeing, msigdb, MSMB, msqc1, muscData, nanotubes, NestLink, OnassisJavaLibs, optimalFlowData, parathyroidSE, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, ppiData, preciseTADhub, ptairData, rcellminerData, RforProteomics, RGMQLlib, RLHub, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, scanMiRData, scATAC.Explorer, SCLCBam, scpdata, scRNAseq, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, spatialLIBD, STexampleData, systemPipeRdata, TabulaMurisData, TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, tuberculosis, VariantToolsData, VectraPolarisData, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations, variants, SingleRBook, aIc, asteRisk, bmstdr, BOSO, cyjShiny, EHRtemporalVariability, ggBubbles, ggcoverage, i2dash, magmaR, metabolomicsR, MetaIntegrator, multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, rjsoncons dependencyCount: 32 Package: biocthis Version: 1.6.0 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: 4b52f5b5845def3e42e33bbad4e04ab2 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (), Marcel Ramos [ctb] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: RELEASE_3_15 git_last_commit: 579720e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biocthis_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocthis_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biocthis_1.6.0.tgz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R importsMe: HubPub suggestsMe: tripr dependencyCount: 51 Package: BiocVersion Version: 3.15.2 Depends: R (>= 4.2.0) License: Artistic-2.0 MD5sum: efb96705287f3333a7732bb2b6318557 NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: master git_last_commit: 818ab03 git_last_commit_date: 2022-03-29 Date/Publication: 2022-03-29 source.ver: src/contrib/BiocVersion_3.15.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocVersion_3.15.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocVersion_3.15.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.64.1 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, knitr, commonmark License: Artistic-2.0 MD5sum: 66cdd6fd08f8c0e6ca5908ee5b6c03ea NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/BiocViews BugReports: https://github.com/Bioconductor/BiocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_15 git_last_commit: 1075b4d git_last_commit_date: 2022-07-18 Date/Publication: 2022-07-19 source.ver: src/contrib/biocViews_1.64.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocViews_1.64.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biocViews_1.64.1.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, monocle, sigFeature, RforProteomics suggestsMe: packFinder dependencyCount: 16 Package: BiocWorkflowTools Version: 1.22.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: bfa6ace8b572faaf3f4720163ffa8db5 NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_15 git_last_commit: 7731927 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocWorkflowTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocWorkflowTools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocWorkflowTools_1.22.0.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow, SingscoreAMLMutations dependencyCount: 58 Package: biodb Version: 1.4.2 Depends: R (>= 4.1.0) Imports: BiocFileCache, R6, RCurl, RSQLite, Rcpp, XML, chk, jsonlite, lgr, lifecycle, methods, openssl, plyr, progress, rappdirs, stats, stringr, tools, withr, yaml LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, xml2, git2r License: AGPL-3 Archs: x64 MD5sum: d913fc4e956932527fd0063f4622e721 NeedsCompilation: yes Title: biodb, a library and a development framework for connecting to chemical and biological databases Description: The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages. biocViews: Software, Infrastructure, DataImport, KEGG Author: Pierrick Roger [aut, cre] (), Alexis Delabrière [ctb] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodb/issues git_url: https://git.bioconductor.org/packages/biodb git_branch: RELEASE_3_15 git_last_commit: bdf8633 git_last_commit_date: 2022-09-21 Date/Publication: 2022-09-22 source.ver: src/contrib/biodb_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodb_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/biodb_1.4.2.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html, vignettes/biodb/inst/doc/new_connector.html, vignettes/biodb/inst/doc/new_entry_field.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects, Creating a new connector class for accessing a database., Creating a new field for entries. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R, vignettes/biodb/inst/doc/new_connector.R, vignettes/biodb/inst/doc/new_entry_field.R importsMe: biodbChebi, biodbExpasy, biodbHmdb, biodbKegg, biodbLipidmaps, biodbMirbase, biodbNcbi, biodbNci, biodbUniprot dependencyCount: 74 Package: biodbChebi Version: 1.2.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.5) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr License: AGPL-3 MD5sum: bedc5fa8c51d17d6230067df7f772aca NeedsCompilation: no Title: biodbChebi, a library for connecting to the ChEBI Database Description: The biodbChebi library provides access to the ChEBI Database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name, mass or other fields. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbChebi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbChebi/issues git_url: https://git.bioconductor.org/packages/biodbChebi git_branch: RELEASE_3_15 git_last_commit: 24c8926 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbChebi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbChebi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbChebi_1.2.0.tgz vignettes: vignettes/biodbChebi/inst/doc/biodbChebi.html vignetteTitles: Introduction to the biodbChebi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbChebi/inst/doc/biodbChebi.R dependencyCount: 75 Package: biodbExpasy Version: 1.0.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, stringr, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 114a7d19dbefbf0456dd1a7ef5a3c315 NeedsCompilation: no Title: biodbExpasy, a library for connecting to Expasy ENZYME database. Description: The biodbExpasy library provides access to Expasy ENZYME database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or comments. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbExpasy git_branch: RELEASE_3_15 git_last_commit: f09985c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbExpasy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbExpasy_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbExpasy_1.0.0.tgz vignettes: vignettes/biodbExpasy/inst/doc/biodbExpasy.html vignetteTitles: Introduction to the biodbExpasy package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbExpasy/inst/doc/biodbExpasy.R dependencyCount: 75 Package: biodbHmdb Version: 1.2.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.3.2), Rcpp LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: fe5972ddfb6a2cda3c47333e5b68db24 NeedsCompilation: yes Title: biodbHmdb, a library for connecting to the HMDB Database Description: The biodbHmdb library is an extension of the biodb framework package that provides access to the HMDB Metabolites database. It allows to download the whole HMDB Metabolites database locally, access entries and search for entries by name or description. A future version of this package will also include a search by mass and mass spectra annotation. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbHmdb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbHmdb/issues git_url: https://git.bioconductor.org/packages/biodbHmdb git_branch: RELEASE_3_15 git_last_commit: 8f80053 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbHmdb_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbHmdb_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbHmdb_1.2.0.tgz vignettes: vignettes/biodbHmdb/inst/doc/biodbHmdb.html vignetteTitles: Introduction to the biodbHmdb package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbHmdb/inst/doc/biodbHmdb.R dependencyCount: 75 Package: biodbKegg Version: 1.2.1 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.4.2), chk, lifecycle Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, igraph, magick, lgr License: AGPL-3 MD5sum: ded53edd194c253f24acb8d4f887f034 NeedsCompilation: no Title: biodbKegg, a library for connecting to the KEGG Database Description: The biodbKegg library is an extension of the biodb framework package that provides access to the KEGG databases Compound, Enzyme, Genes, Module, Orthology and Reaction. It allows to retrieve entries by their accession numbers. Web services like "find", "list" and "findExactMass" are also available. Some functions for navigating along the pathways have also been implemented. biocViews: Software, Infrastructure, DataImport, Pathways, KEGG Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbKegg VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbKegg/issues git_url: https://git.bioconductor.org/packages/biodbKegg git_branch: RELEASE_3_15 git_last_commit: 481fcb5 git_last_commit_date: 2022-09-22 Date/Publication: 2022-09-25 source.ver: src/contrib/biodbKegg_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbKegg_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbKegg_1.2.1.tgz vignettes: vignettes/biodbKegg/inst/doc/biodbKegg.html vignetteTitles: Introduction to the biodbKegg package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbKegg/inst/doc/biodbKegg.R dependencyCount: 75 Package: biodbLipidmaps Version: 1.2.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), lifecycle, R6 Suggests: BiocStyle, lgr, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr License: AGPL-3 MD5sum: 88766db9ab8621981ca95b048df717f8 NeedsCompilation: no Title: biodbLipidmaps, a library for connecting to the Lipidmaps Structure database Description: The biodbLipidmaps library provides access to the Lipidmaps Structure Database, using biodb package framework. It allows to retrieve entries by their accession number, and run web the services lmsdSearch and lmsdRecord. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbLipidmaps VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbLipidmaps/issues git_url: https://git.bioconductor.org/packages/biodbLipidmaps git_branch: RELEASE_3_15 git_last_commit: 484c60e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbLipidmaps_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbLipidmaps_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbLipidmaps_1.2.0.tgz vignettes: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.html vignetteTitles: An introduction to biodbLipidmaps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.R dependencyCount: 75 Package: biodbMirbase Version: 1.0.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, stringr, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: a6c43b9286af43dafd1656b9fa08c60d NeedsCompilation: no Title: biodbMirbase, a library for connecting to miRBase mature database Description: The biodbMirbase library is an extension of the biodb framework package, that provides access to miRBase mature database. It allows to retrieve entries by their accession number, and run specific web services. Description: The biodbMirbase library provides access to the miRBase Database, using biodb package framework. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbMirbase git_branch: RELEASE_3_15 git_last_commit: 568294f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbMirbase_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbMirbase_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbMirbase_1.0.0.tgz vignettes: vignettes/biodbMirbase/inst/doc/biodbMirbase.html vignetteTitles: Introduction to the biodbMirbase package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbMirbase/inst/doc/biodbMirbase.R dependencyCount: 75 Package: biodbNcbi Version: 1.0.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), R6, XML, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 9d69de438c6a44c6a778967c05af2875 NeedsCompilation: no Title: biodbNcbi, a library for connecting to NCBI Databases. Description: The biodbNcbi library provides access to the NCBI databases CCDS, Gene, Pubchem Comp and Pubchem Subst, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or mass. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbNcbi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbNCbi/issues git_url: https://git.bioconductor.org/packages/biodbNcbi git_branch: RELEASE_3_15 git_last_commit: 7e99f1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbNcbi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbNcbi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbNcbi_1.0.0.tgz vignettes: vignettes/biodbNcbi/inst/doc/biodbNcbi.html vignetteTitles: Introduction to the biodbNcbi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNcbi/inst/doc/biodbNcbi.R dependencyCount: 75 Package: biodbNci Version: 1.0.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, Rcpp, chk LinkingTo: Rcpp, testthat Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: 06ecda4e04f0a2ce05a3e03f8a0f2bf3 NeedsCompilation: yes Title: biodbNci, a library for connecting to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database Description: The biodbNci library is an extension of the biodb framework package. It provides access to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database. It allows to retrieve entries by their accession number, and run specific web services. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbNci git_branch: RELEASE_3_15 git_last_commit: 538228c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biodbNci_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbNci_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbNci_1.0.0.tgz vignettes: vignettes/biodbNci/inst/doc/biodbNci.html vignetteTitles: Introduction to the biodbNci package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNci/inst/doc/biodbNci.R dependencyCount: 75 Package: biodbUniprot Version: 1.2.1 Depends: R (>= 4.1.0) Imports: R6, biodb (>= 1.4.2) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr, covr License: AGPL-3 MD5sum: f352b23ef0c0068f19c7ab8fcb88de47 NeedsCompilation: no Title: biodbUniprot, a library for connecting to the Uniprot Database Description: The biodbUniprot library is an extension of the biodb framework package. It provides access to the UniProt database. It allows to retrieve entries by their accession number, and run web service queries for searching for entries. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbUniprot VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbUniprot/issues git_url: https://git.bioconductor.org/packages/biodbUniprot git_branch: RELEASE_3_15 git_last_commit: 64fb603 git_last_commit_date: 2022-09-21 Date/Publication: 2022-09-25 source.ver: src/contrib/biodbUniprot_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbUniprot_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbUniprot_1.2.1.tgz vignettes: vignettes/biodbUniprot/inst/doc/biodbUniprot.html vignetteTitles: Introduction to the biodbUniprot package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbUniprot/inst/doc/biodbUniprot.R dependencyCount: 75 Package: bioDist Version: 1.68.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 573a5214c0d88c3236c169004261f009 NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: RELEASE_3_15 git_last_commit: 51ae6c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bioDist_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioDist_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bioDist_1.68.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile dependencyCount: 7 Package: biomaRt Version: 2.52.0 Depends: methods Imports: utils, XML (>= 3.99-0.7), AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 Suggests: BiocStyle, knitr, rmarkdown, testthat, mockery License: Artistic-2.0 MD5sum: 7cd39e4c624ed157994028bbcd81d3c1 NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] () Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_15 git_last_commit: cf4932a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biomaRt_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomaRt_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biomaRt_2.52.0.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: BrainSABER, chromPlot, coMET, customProDB, DrugVsDisease, genefu, GenomicOZone, MineICA, NetSAM, PPInfer, PSICQUIC, RepViz, VegaMC, annotation importsMe: ArrayExpressHTS, ASpediaFI, BadRegionFinder, BgeeCall, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, conclus, dagLogo, DEXSeq, diffloop, DominoEffect, easyRNASeq, EDASeq, ELMER, epimutacions, FRASER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, gpart, Gviz, hermes, InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, scPipe, seq2pathway, SeqGSEA, sitadela, SPLINTER, SPONGE, surfaltr, SWATH2stats, TCGAbiolinks, TEKRABber, TFEA.ChIP, TimiRGeN, transcriptogramer, trena, ViSEAGO, yarn, ExpHunterSuite, TCGAWorkflow, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, intePareto, kangar00, liayson, MantaID, seeker, snplist, utr.annotation suggestsMe: AnnotationForge, bioassayR, celda, cellTree, chromstaR, ClusterJudge, CNVgears, ctgGEM, cTRAP, epistack, fedup, FELLA, GRaNIE, h5vc, MAGeCKFlute, martini, massiR, MethReg, MineICA, miQC, MiRaGE, MutationalPatterns, netSmooth, oligo, OrganismDbi, piano, Pigengene, progeny, PubScore, R3CPET, Rcade, RnBeads, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, trackViewer, wiggleplotr, zinbwave, BloodCancerMultiOmics2017, ccTutorial, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, DGEobj, DGEobj.utils, dnapath, MoBPS, Patterns, Platypus, R.SamBada, scDiffCom, SNPassoc dependencyCount: 70 Package: biomformat Version: 1.24.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: c2caea37604316976d7c43555b6e28c4 NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: RELEASE_3_15 git_last_commit: 4e14692 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biomformat_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomformat_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biomformat_1.24.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: animalcules, microbiomeExplorer, microbiomeMarker, phyloseq suggestsMe: metagenomeSeq, mia, MicrobiotaProcess, metacoder, PLNmodels dependencyCount: 14 Package: BioMM Version: 1.12.0 Depends: R (>= 3.6) Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot, imager, topGO, xlsx Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: da69d082b6aee83e1462c5efd8b5474b NeedsCompilation: no Title: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data Description: The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information. biocViews: Genetics, Classification, Regression, Pathways, GO, Software Author: Junfang Chen and Emanuel Schwarz Maintainer: Junfang Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioMM git_branch: RELEASE_3_15 git_last_commit: 0b78103 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioMM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioMM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioMM_1.12.0.tgz vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html vignetteTitles: BioMMtutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R dependencyCount: 146 Package: BioMVCClass Version: 1.64.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 13be2784a2a2c4841154755f11855e55 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: RELEASE_3_15 git_last_commit: d1ec821 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioMVCClass_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioMVCClass_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioMVCClass_1.64.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: biomvRCNS Version: 1.36.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: x64 MD5sum: 002d84b523c2de16500bb11e0ccb7b53 NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: RELEASE_3_15 git_last_commit: 0e9de50 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biomvRCNS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomvRCNS_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biomvRCNS_1.36.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 147 Package: BioNERO Version: 1.4.2 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, matrixStats, sva, RColorBrewer, ComplexHeatmap, ggplot2, ggrepel, patchwork, reshape2, igraph, ggnetwork, intergraph, networkD3, ggnewscale, NetRep, stats, grDevices, graphics, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2, covr License: GPL-3 MD5sum: b8bbaf8d7ffce65350499680cacb58c2 NeedsCompilation: no Title: Biological Network Reconstruction Omnibus Description: BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks. biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology, GraphAndNetwork, Preprocessing, Network Author: Fabricio Almeida-Silva [cre, aut] (), Thiago Venancio [aut] () Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/BioNERO VignetteBuilder: knitr BugReports: https://github.com/almeidasilvaf/BioNERO/issues git_url: https://git.bioconductor.org/packages/BioNERO git_branch: RELEASE_3_15 git_last_commit: ac0a4ae git_last_commit_date: 2022-09-04 Date/Publication: 2022-09-04 source.ver: src/contrib/BioNERO_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNERO_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNERO_1.4.2.tgz vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html vignetteTitles: Gene coexpression network inference, Gene regulatory network inference with BioNERO, Network comparison: consensus modules and module preservation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R importsMe: cageminer dependencyCount: 161 Package: BioNet Version: 1.56.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 47b4d55f86deee606fd90ddf206dec6d NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: RELEASE_3_15 git_last_commit: a9a9190 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioNet_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNet_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNet_1.56.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: SMITE suggestsMe: SANTA, mwcsr dependencyCount: 53 Package: BioNetStat Version: 1.16.1 Depends: R (>= 4.0), shiny, igraph, shinyBS, pathview, DT Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr, rmarkdown, markdown License: GPL (>= 3) MD5sum: 6aa7db7c2ccd6086273bd9136ea9f7f1 NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment, ImmunoOncology Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr, rmarkdown BugReports: http://github.com/jardimViniciusC/BioNetStat/issues git_url: https://git.bioconductor.org/packages/BioNetStat git_branch: RELEASE_3_15 git_last_commit: 0b6aaac git_last_commit_date: 2022-06-26 Date/Publication: 2022-06-26 source.ver: src/contrib/BioNetStat_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNetStat_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNetStat_1.16.1.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_pt.html, vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_us.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 3. Tutorial para o console do R, 2. R console tutorial, 1. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 142 Package: BioPlex Version: 1.2.3 Depends: R (>= 4.1.0), SummarizedExperiment Imports: BiocFileCache, GenomicRanges, GenomeInfoDb, GEOquery, graph, methods, utils Suggests: AnnotationDbi, AnnotationHub, BiocStyle, ExperimentHub, GenomicFeatures, S4Vectors, depmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: 74883d42d02795c1dadaddae810fdc69 NeedsCompilation: no Title: R-side access to BioPlex protein-protein interaction data Description: The BioPlex package implements access to the BioPlex protein-protein interaction networks and related resources from within R. Besides protein-protein interaction networks for HEK293 and HCT116 cells, this includes access to CORUM protein complex data, and transcriptome and proteome data for the two cell lines. Functionality focuses on importing the various data resources and storing them in dedicated Bioconductor data structures, as a foundation for integrative downstream analysis of the data. biocViews: DataImport, DataRepresentation, GeneExpression, GraphAndNetwork, MassSpectrometry, Network, Transcriptomics, Proteomics Author: Ludwig Geistlinger [aut, cre], Robert Gentleman [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/ccb-hms/BioPlex VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/BioPlex/issues git_url: https://git.bioconductor.org/packages/BioPlex git_branch: RELEASE_3_15 git_last_commit: ba606c6 git_last_commit_date: 2022-06-13 Date/Publication: 2022-06-14 source.ver: src/contrib/BioPlex_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioPlex_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.2/BioPlex_1.2.3.tgz vignettes: vignettes/BioPlex/inst/doc/BasicChecks.html, vignettes/BioPlex/inst/doc/BioPlex.html vignetteTitles: 2. Data checks, 1. Data retrieval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioPlex/inst/doc/BasicChecks.R, vignettes/BioPlex/inst/doc/BioPlex.R dependencyCount: 83 Package: BioQC Version: 1.24.0 Depends: R (>= 3.5.0), Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer License: GPL (>=3) + file LICENSE Archs: x64 MD5sum: 35fdc3b3c55c611f6d92a65563232c23 NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] Maintainer: Jitao David Zhang URL: https://accio.github.io/BioQC VignetteBuilder: knitr BugReports: https://accio.github.io/BioQC/issues git_url: https://git.bioconductor.org/packages/BioQC git_branch: RELEASE_3_15 git_last_commit: ca8b2f2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioQC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioQC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioQC_1.24.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-introduction.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc-simulation.html, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html, vignettes/BioQC/inst/doc/BioQC.html vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test, BioQC: Detect tissue heterogeneity in gene expression data, Using BioQC with signed genesets, BioQC-benchmark: Testing Efficiency,, Sensitivity and Specificity of BioQC on simulated and real-world data, Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests, BioQC-kidney: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-introduction.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc-simulation.R, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R, vignettes/BioQC/inst/doc/BioQC.R dependencyCount: 13 Package: biosigner Version: 1.24.2 Imports: Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 3467498d8cace2c7dde371324725a9ae NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] () Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.3389/fmolb.2016.00026 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_15 git_last_commit: 8f8c386 git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/biosigner_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/biosigner_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/biosigner_1.24.2.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: biosigner-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R importsMe: multiSight dependencyCount: 72 Package: Biostrings Version: 2.64.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.30.1), XVector (>= 0.29.2), GenomeInfoDb Imports: methods, utils, grDevices, graphics, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: x64 MD5sum: 71a78d5b184e315b7d4565f783f35d6e NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès URL: https://bioconductor.org/packages/Biostrings BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_15 git_last_commit: ffe263e git_last_commit_date: 2022-08-17 Date/Publication: 2022-08-18 source.ver: src/contrib/Biostrings_2.64.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Biostrings_2.64.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Biostrings_2.64.1.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, msa, muscle, oligo, ORFhunteR, periodicDNA, pqsfinder, PWMEnrich, qrqc, QSutils, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, rRDP, Rsamtools, RSVSim, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX, seqbias, ShortRead, SICtools, SimFFPE, ssviz, Structstrings, svaNUMT, systemPipeR, topdownr, TreeSummarizedExperiment, triplex, VarCon, FDb.FANTOM4.promoters.hg19, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP, JASPAR2014, NestLink, generegulation, sequencing, CleanBSequences, pagoo, SimRAD, STRMPS, SubVis importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, appreci8R, ArrayExpressHTS, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall, biovizBase, brainflowprobes, branchpointer, BSgenome, bsseq, BUMHMM, BUSpaRse, CellaRepertorium, CellBarcode, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, cleanUpdTSeq, cliProfiler, CNEr, CNVfilteR, cogeqc, compEpiTools, consensusDE, coRdon, crisprBase, crisprBowtie, crisprScore, CrispRVariants, customProDB, dada2, dagLogo, DAMEfinder, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, easyRNASeq, EDASeq, enhancerHomologSearch, ensembldb, ensemblVEP, EpiTxDb, esATAC, eudysbiome, EventPointer, FastqCleaner, FLAMES, GA4GHclient, gcapc, gcrma, genbankr, GeneRegionScan, genomation, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicScores, genphen, GenVisR, ggbio, ggmsa, girafe, gmapR, gmoviz, GRaNIE, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiLDA, HiTC, HTSeqGenie, icetea, idpr, IMMAN, IntEREst, InterMineR, IONiseR, ipdDb, IsoformSwitchAnalyzeR, KEGGREST, LinTInd, LowMACA, LymphoSeq, m6Aboost, MACPET, MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, memes, MesKit, metaseqR2, methimpute, methylPipe, methylscaper, mia, microbiome, microbiomeMarker, MicrobiotaProcess, microRNA, MMDiff2, monaLisa, Motif2Site, motifbreakR, motifcounter, motifmatchr, motifStack, MSA2dist, MSnID, MSstatsLiP, MSstatsPTM, multicrispr, MungeSumstats, musicatk, MutationalPatterns, NanoStringNCTools, ngsReports, nucleR, NxtIRFcore, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase, packFinder, pdInfoBuilder, PhyloProfile, phyloseq, pipeFrame, podkat, polyester, primirTSS, proBAMr, procoil, ProteoDisco, PureCN, Pviz, qPLEXanalyzer, qrqc, qsea, QuasR, r3Cseq, ramwas, RCAS, Rcpi, recoup, regioneR, regutools, REMP, Repitools, rfaRm, rGADEM, RiboCrypt, ribosomeProfilingQC, RNAmodR, rprimer, Rqc, rtracklayer, sarks, scanMiR, scanMiRApp, scmeth, SCOPE, scoreInvHap, scruff, seqArchR, SeqArray, seqPattern, SGSeq, signeR, SigsPack, single, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spiky, SPLINTER, sscu, StructuralVariantAnnotation, supersigs, surfaltr, svaRetro, synapter, SynExtend, SynMut, TAPseq, TarSeqQC, TFBSTools, transite, trena, tRNA, tRNAdbImport, tRNAscanImport, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, universalmotif, VariantAnnotation, VariantExperiment, VariantFiltering, VariantTools, wavClusteR, XNAString, YAPSA, EuPathDB, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, microbiomeDataSets, pd.atdschip.tiling, PhyloProfileData, systemPipeRdata, ActiveDriverWGS, alakazam, BALCONY, BASiNET, BASiNETEntropy, biomartr, copyseparator, crispRdesignR, CSESA, deepredeff, dowser, EncDNA, ensembleTax, EpiSemble, epitopR, genBaRcode, geneHapR, ggcoverage, hoardeR, ICAMS, immuneSIM, kibior, metaCluster, MitoHEAR, MixviR, PACVr, Platypus, PredCRG, ptm, seqmagick, simMP, SMITIDstruct, SNPassoc, TrustVDJ, utr.annotation, vhcub suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS, BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles, GenomicRanges, GWASTools, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, spatzie, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, bbl, bio3d, datelife, DDPNA, file2meco, gkmSVM, maGUI, minSNPs, msaR, NameNeedle, phangorn, polyRAD, protr, rDNAse, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 17 Package: BioTIP Version: 1.10.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: a875694fd62d3ed14dffeb12ff7cb434 NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Yuxi (Jennifer) Sun , Zhezhen Wang , and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: RELEASE_3_15 git_last_commit: 1ae6341 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BioTIP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioTIP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioTIP_1.10.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 67 Package: biotmle Version: 1.20.0 Depends: R (>= 4.0) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: MIT + file LICENSE MD5sum: 3d60fcfb74549ed5a8dee1f3cc7f4ad1 NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (), Alan Hubbard [aut, ths] (), Mark van der Laan [aut, ths] (), Weixin Cai [ctb] (), Philippe Boileau [ctb] () Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: RELEASE_3_15 git_last_commit: a6bfb43 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biotmle_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biotmle_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biotmle_1.20.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 98 Package: biovizBase Version: 1.44.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 32bf4e424dcc0c466e003204066a0424 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_15 git_last_commit: a8f05c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biovizBase_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biovizBase_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biovizBase_1.44.0.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinderPlot, NanoStringNCTools, R3CPET, regionReport, StructuralVariantAnnotation, Signac dependencyCount: 144 Package: BiRewire Version: 3.28.0 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: f5f114e077adde02b1926ec23b3e8a53 NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted. biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: RELEASE_3_15 git_last_commit: f901a6f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiRewire_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiRewire_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiRewire_3.28.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 15 Package: biscuiteer Version: 1.10.0 Depends: R (>= 4.1.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, IRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rmarkdown, markdown, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10 License: GPL-3 MD5sum: 25183fea0e6f2103d5f17f7ced5ab3b2 NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut], Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut] Maintainer: Jacob Morrison URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: RELEASE_3_15 git_last_commit: 4e21745 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/biscuiteer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biscuiteer_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biscuiteer_1.10.0.tgz vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 193 Package: BiSeq Version: 1.36.0 Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 0f80bd74c5d1a7c05225aa404ef0ddce NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: RELEASE_3_15 git_last_commit: b5cd494 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiSeq_1.36.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata dependencyCount: 86 Package: BitSeq Version: 1.40.0 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges, methods, utils LinkingTo: Rhtslib (>= 1.15.5) Suggests: BiocStyle License: Artistic-2.0 + file LICENSE Archs: x64 MD5sum: 048133b8f2be4fe0ccd2463b80f08e2c NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/BitSeq git_branch: RELEASE_3_15 git_last_commit: 66cfe24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BitSeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BitSeq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BitSeq_1.40.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R dependencyCount: 30 Package: blacksheepr Version: 1.10.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: adf4beb2886a1243151113803c51d0e2 NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: RELEASE_3_15 git_last_commit: 8e46c00 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/blacksheepr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/blacksheepr_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/blacksheepr_1.10.0.tgz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 129 Package: blima Version: 1.30.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 Archs: x64 MD5sum: b6583f66242e61ad036236c1a747f7ea NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: RELEASE_3_15 git_last_commit: 4841dee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/blima_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/blima_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/blima_1.30.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R suggestsMe: blimaTestingData dependencyCount: 81 Package: BLMA Version: 1.20.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 648177d2269772622a4c0e9be5f5450c NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Hung Nguyen git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_15 git_last_commit: ff6c3fd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BLMA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BLMA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BLMA_1.20.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R dependencyCount: 73 Package: BloodGen3Module Version: 1.4.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics, stats, grDevices, circlize, testthat, ComplexHeatmap(>= 1.99.8), ggplot2, matrixStats, gtools, reshape2, preprocessCore, randomcoloR, V8, limma Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 8317dc61c7d0a1045c287959a300292b NeedsCompilation: no Title: This R package for performing module repertoire analyses and generating fingerprint representations Description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows. biocViews: Software, Visualization, GeneExpression Author: Darawan Rinchai [aut, cre] () Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: RELEASE_3_15 git_last_commit: c596a7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BloodGen3Module_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BloodGen3Module_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BloodGen3Module_1.4.0.tgz vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R dependencyCount: 145 Package: bluster Version: 1.6.0 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster License: GPL-3 Archs: x64 MD5sum: 203fe8bddeb6aa7ef0da46674ce708d1 NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: RELEASE_3_15 git_last_commit: ff86c7d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bluster_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bluster_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bluster_1.6.0.tgz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: scDblFinder, scran, Canek suggestsMe: batchelor, ChromSCape, dittoSeq, mbkmeans, mumosa, SingleRBook dependencyCount: 28 Package: bnbc Version: 1.18.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: b4df0be5620d3b8288912973286f025d NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: RELEASE_3_15 git_last_commit: d4209a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bnbc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bnbc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bnbc_1.18.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 89 Package: bnem Version: 1.4.0 Depends: R (>= 4.1) Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster, flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods, utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown Suggests: knitr, BiocGenerics License: GPL-3 MD5sum: 0d43ebafcb2c46e726fb4ef3ad29bb5c NeedsCompilation: no Title: Training of logical models from indirect measurements of perturbation experiments Description: bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate). biocViews: Pathways, SystemsBiology, NetworkInference, Network, GeneExpression, GeneRegulation, Preprocessing Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/MartinFXP/bnem/ VignetteBuilder: knitr BugReports: https://github.com/MartinFXP/bnem/issues git_url: https://git.bioconductor.org/packages/bnem git_branch: RELEASE_3_15 git_last_commit: 140e9fb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bnem_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bnem_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bnem_1.4.0.tgz vignettes: vignettes/bnem/inst/doc/bnem.html vignetteTitles: bnem.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnem/inst/doc/bnem.R dependencyCount: 172 Package: BOBaFIT Version: 1.0.1 Depends: R (>= 2.10) Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr, GenomicRanges, ggforce, stringr, plyranges, methods, utils, magrittr Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0), utils, testthat License: GPL (>= 3) MD5sum: 4f297679bbfa3c427369710eb50603f4 NeedsCompilation: no Title: Refitting diploid region profiles using a clustering procedure Description: This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster. biocViews: CopyNumberVariation, Clustering, Visualization, Normalization, Software Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza Solli [aut] Maintainer: Gaia Mazzocchetti URL: https://github.com/andrea-poletti-unibo/BOBaFIT VignetteBuilder: knitr BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues git_url: https://git.bioconductor.org/packages/BOBaFIT git_branch: RELEASE_3_15 git_last_commit: 16167aa git_last_commit_date: 2022-07-13 Date/Publication: 2022-07-14 source.ver: src/contrib/BOBaFIT_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BOBaFIT_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BOBaFIT_1.0.1.tgz vignettes: vignettes/BOBaFIT/inst/doc/BOBaFIT.html, vignettes/BOBaFIT/inst/doc/Data-Preparation.html vignetteTitles: BOBaFIT.Rmd, Data preparation using TCGA-BRCA database.Rmd hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BOBaFIT/inst/doc/BOBaFIT.R, vignettes/BOBaFIT/inst/doc/Data-Preparation.R dependencyCount: 163 Package: borealis Version: 1.0.1 Depends: R (>= 4.2.0), Biobase Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist, bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot, dplyr, rlang, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL-3 MD5sum: 106751751a95cb34014b23d2c134cf56 NeedsCompilation: no Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution Description: Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses. biocViews: Sequencing, Coverage, DNAMethylation, DifferentialMethylation Author: Garrett Jenkinson [aut, cre] () Maintainer: Garrett Jenkinson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/borealis git_branch: RELEASE_3_15 git_last_commit: c9ce514 git_last_commit_date: 2022-05-24 Date/Publication: 2022-05-26 source.ver: src/contrib/borealis_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/borealis_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/borealis_1.0.1.tgz vignettes: vignettes/borealis/inst/doc/borealis.html vignetteTitles: Borealis outlier methylation detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/borealis/inst/doc/borealis.R dependencyCount: 105 Package: BPRMeth Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE Archs: x64 MD5sum: e7a4bd866b58a48975249e14b7564d96 NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: RELEASE_3_15 git_last_commit: f29c5f1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BPRMeth_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BPRMeth_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BPRMeth_1.22.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R dependsOnMe: Melissa dependencyCount: 94 Package: BRAIN Version: 1.42.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: a3654d0f78e8dc12fea431052288dc11 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: RELEASE_3_15 git_last_commit: 83ef4e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BRAIN_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BRAIN_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BRAIN_1.42.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter, RforProteomics dependencyCount: 22 Package: brainflowprobes Version: 1.10.0 Depends: R (>= 3.6.0) Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder (>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>= 1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils, grDevices, GenomicState (>= 0.99.7) Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: 61c9c0ea62c37dddb5cba4a7a5f56a4f NeedsCompilation: no Title: Plots and annotation for choosing BrainFlow target probe sequence Description: Use these functions to characterize genomic regions for BrainFlow target probe design. biocViews: Coverage, Visualization, ExperimentalDesign, Transcriptomics, FlowCytometry, GeneTarget Author: Amanda Price [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Amanda Price URL: https://github.com/LieberInstitute/brainflowprobes VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/brainflowprobes git_url: https://git.bioconductor.org/packages/brainflowprobes git_branch: RELEASE_3_15 git_last_commit: c915801 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/brainflowprobes_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/brainflowprobes_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/brainflowprobes_1.10.0.tgz vignettes: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html vignetteTitles: brainflowprobes users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R dependencyCount: 190 Package: BrainSABER Version: 1.6.0 Depends: R (>= 4.1.0), biomaRt, SummarizedExperiment Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache, shiny Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr, plotly, rmarkdown License: Artistic-2.0 MD5sum: e3e2e2bd16beb486dacfd6b3eb45c43d NeedsCompilation: no Title: Brain Span Atlas in Biobase Expressionset R toolset Description: The Allen Institute for Brain Science provides an RNA sequencing (RNA-Seq) data resource for studying transcriptional mechanisms involved in human brain development known as BrainSpan. BrainSABER is an R package that facilitates comparison of user data with the various developmental stages and brain structures found in the BrainSpan atlas by generating dynamic similarity heatmaps for the two data sets. It also provides a self-validating container for user data. biocViews: GeneExpression, Visualization, Sequencing Author: Carrie Minette [aut], Evgeni Radichev [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BrainSABER git_branch: RELEASE_3_15 git_last_commit: 70551c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BrainSABER_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BrainSABER_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BrainSABER_1.6.0.tgz vignettes: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.html vignetteTitles: BrainSABER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.R dependencyCount: 96 Package: branchpointer Version: 1.22.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: 3a548274c0de15dad2173ac98b57bffd NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: RELEASE_3_15 git_last_commit: e74a87d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/branchpointer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/branchpointer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/branchpointer_1.22.0.tgz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 148 Package: breakpointR Version: 1.14.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: 288cde8cae5d98b933b18e5f0a9274e7 NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: RELEASE_3_15 git_last_commit: b123c8a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/breakpointR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/breakpointR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/breakpointR_1.14.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 73 Package: brendaDb Version: 1.10.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, curl, xml2, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE Archs: x64 MD5sum: aa75abf05805dfc4dbc49a450f28278d NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] () Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: RELEASE_3_15 git_last_commit: 32fe213 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/brendaDb_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/brendaDb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/brendaDb_1.10.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 61 Package: BRGenomics Version: 1.8.0 Depends: R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors Imports: GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings License: Artistic-2.0 MD5sum: 0b7809e8b02928596ff43c1a6416f6a1 NeedsCompilation: no Title: Tools for the Efficient Analysis of High-Resolution Genomics Data Description: This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features. biocViews: Software, DataImport, Sequencing, Coverage, RNASeq, ATACSeq, ChIPSeq, Transcription, GeneRegulation, GeneExpression, Normalization Author: Mike DeBerardine [aut, cre] Maintainer: Mike DeBerardine URL: https://mdeber.github.io VignetteBuilder: knitr BugReports: https://github.com/mdeber/BRGenomics/issues git_url: https://git.bioconductor.org/packages/BRGenomics git_branch: RELEASE_3_15 git_last_commit: c879a27 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BRGenomics_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BRGenomics_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BRGenomics_1.8.0.tgz vignettes: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.html, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.html, vignettes/BRGenomics/inst/doc/GettingStarted.html, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.html, vignettes/BRGenomics/inst/doc/ImportingProcessingData.html, vignettes/BRGenomics/inst/doc/Overview.html, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.html, vignettes/BRGenomics/inst/doc/SequenceExtraction.html, vignettes/BRGenomics/inst/doc/SignalCounting.html, vignettes/BRGenomics/inst/doc/SpikeInNormalization.html vignetteTitles: Analyzing Multiple Datasets, DESeq2 with Global Perturbations, Getting Started, Importing and Modifying Annotations, Importing and Processing Data, Overview, Profile Plots and Bootstrapping, Sequence Extraction, Signal Counting, Spike-in Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.R, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.R, vignettes/BRGenomics/inst/doc/GettingStarted.R, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.R, vignettes/BRGenomics/inst/doc/ImportingProcessingData.R, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.R, vignettes/BRGenomics/inst/doc/SequenceExtraction.R, vignettes/BRGenomics/inst/doc/SignalCounting.R, vignettes/BRGenomics/inst/doc/SpikeInNormalization.R importsMe: EpiCompare dependencyCount: 101 Package: bridge Version: 1.60.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: x64 MD5sum: 55d67191b361d870d21ff27185084569 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/bridge git_branch: RELEASE_3_15 git_last_commit: a2cb645 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bridge_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bridge_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bridge_1.60.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R dependencyCount: 1 Package: BridgeDbR Version: 2.6.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 9f2bc6c25c0c37d162243107ed8a51f7 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Anwesha Bohler , Lars Eijssen Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: RELEASE_3_15 git_last_commit: 9d8b2b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BridgeDbR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BridgeDbR_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BridgeDbR_2.6.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 3 Package: BrowserViz Version: 2.18.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: da2ead794c239838a6fca94a7102acf8 NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/paul-shannon/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_15 git_last_commit: c4613ca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BrowserViz_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BrowserViz_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BrowserViz_2.18.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 13 Package: BSgenome Version: 1.64.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, matrixStats, BiocGenerics, S4Vectors, IRanges, XVector (>= 0.29.3), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocManager, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: 3547bb1a2b0543e4f63033334176ca09 NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_15 git_last_commit: 59cdebd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BSgenome_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BSgenome_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BSgenome_1.64.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, rGADEM, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, cliProfiler, crisprBowtie, crisprBwa, CRISPRseek, crisprseekplus, diffHic, dpeak, enhancerHomologSearch, enrichTF, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, IsoformSwitchAnalyzeR, m6Aboost, MADSEQ, methrix, MethylSeekR, MMDiff2, monaLisa, Motif2Site, motifbreakR, motifmatchr, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, NxtIRFcore, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP, Repitools, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spatzie, spiky, TAPseq, TFBSTools, trena, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, XNAString, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ActiveDriverWGS, deconstructSigs, ggcoverage, ICAMS, simMP suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, easyRNASeq, eisaR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, sitadela, SNPlocs.Hsapiens.dbSNP.20101109, gkmSVM, sigminer, Signac, SNPassoc dependencyCount: 45 Package: bsseq Version: 1.32.0 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5 LinkingTo: Rcpp, beachmat Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat (>= 1.5.2), BatchJobs License: Artistic-2.0 Archs: x64 MD5sum: c4d76e45df542af780ccfdf9401abfff NeedsCompilation: yes Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_15 git_last_commit: a0c1eac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bsseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bsseq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bsseq_1.32.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData importsMe: borealis, DMRcate, methylCC, methylSig, MIRA, NanoMethViz, scmeth suggestsMe: methrix, tissueTreg dependencyCount: 74 Package: BubbleTree Version: 2.26.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: b180006141ce753850b7077c74959a9d NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: RELEASE_3_15 git_last_commit: e421c4f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BubbleTree_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BubbleTree_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BubbleTree_2.26.0.tgz vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R dependencyCount: 161 Package: BufferedMatrix Version: 1.60.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: x64 MD5sum: 830bcb4051f50bc8193403e19289a49b NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: RELEASE_3_15 git_last_commit: 3bff61b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BufferedMatrix_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BufferedMatrix_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BufferedMatrix_1.60.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.60.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: x64 MD5sum: 9f201ae471b7c354b8112efd971f3874 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: RELEASE_3_15 git_last_commit: 2da9528 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BufferedMatrixMethods_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BufferedMatrixMethods_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BufferedMatrixMethods_1.60.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.2.2 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5d7e185baa6a8db2f4d1a2956e4d942d NeedsCompilation: no Title: R-side access to published microbial signatures from BugSigDB Description: The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT. biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/bugsigdbr VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bugsigdbr/issues git_url: https://git.bioconductor.org/packages/bugsigdbr git_branch: RELEASE_3_15 git_last_commit: 0bedd3e git_last_commit_date: 2022-09-06 Date/Publication: 2022-09-08 source.ver: src/contrib/bugsigdbr_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/bugsigdbr_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/bugsigdbr_1.2.2.tgz vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html vignetteTitles: R-side access to BugSigDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R dependencyCount: 53 Package: BUMHMM Version: 1.20.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: 9385d0071161d14fcd6e3c865a1f9251 NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: RELEASE_3_15 git_last_commit: 60fd865 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BUMHMM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUMHMM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUMHMM_1.20.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 122 Package: bumphunter Version: 1.38.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 8a4a06a8bbd33f95feba65a14d245885 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb], Tamilselvi Guharaj [cre] Maintainer: Tamilselvi Guharaj URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_15 git_last_commit: 06e2fa8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/bumphunter_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bumphunter_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bumphunter_1.38.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: brainflowprobes, coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions, epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 103 Package: BumpyMatrix Version: 1.4.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 89ed8db5c8ca853b5a551331dda23105 NeedsCompilation: no Title: Bumpy Matrix of Non-Scalar Objects Description: Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. biocViews: Software, Infrastructure, DataRepresentation Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/BumpyMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/BumpyMatrix/issues git_url: https://git.bioconductor.org/packages/BumpyMatrix git_branch: RELEASE_3_15 git_last_commit: e2aebf4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BumpyMatrix_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BumpyMatrix_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BumpyMatrix_1.4.0.tgz vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html vignetteTitles: The BumpyMatrix class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R importsMe: CoreGx, MouseGastrulationData suggestsMe: ggspavis, SpatialExperiment, STexampleData dependencyCount: 12 Package: BUS Version: 1.52.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: x64 MD5sum: d8ed38674ed27e6fba61b3fb0bd38ce7 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: RELEASE_3_15 git_last_commit: 774a951 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BUS_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUS_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUS_1.52.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.14.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 4e0d375f3a5d0edb8403d8924b8812d3 NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: RELEASE_3_15 git_last_commit: 58b3a2b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BUScorrect_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUScorrect_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUScorrect_1.14.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 29 Package: BUSpaRse Version: 1.10.0 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 399162f416ee3314862f3e3568eee705 NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: RELEASE_3_15 git_last_commit: ad2ccde git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BUSpaRse_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUSpaRse_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUSpaRse_1.10.0.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 122 Package: BUSseq Version: 1.2.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: bfc4dbac730d1bf8a8d9d4cb114958ce NeedsCompilation: yes Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript. biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing Author: Fangda Song [aut, cre] (), Ga Ming Chan [aut], Yingying Wei [aut] () Maintainer: Fangda Song URL: https://github.com/songfd2018/BUSseq VignetteBuilder: knitr BugReports: https://github.com/songfd2018/BUSseq/issues git_url: https://git.bioconductor.org/packages/BUSseq git_branch: RELEASE_3_15 git_last_commit: 4caea40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BUSseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUSseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUSseq_1.2.0.tgz vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R dependencyCount: 30 Package: CAEN Version: 1.4.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 87dc7a207c3721b0a92f234a6103b3e0 NeedsCompilation: no Title: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq Description: With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples. biocViews: DifferentialExpression, Sequencing, Classification, RNASeq, ATACSeq, SingleCell, GeneExpression, RIPSeq Author: Zhou Yan [aut, cre] Maintainer: Zhou Yan <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAEN git_branch: RELEASE_3_15 git_last_commit: 781f533 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CAEN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAEN_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAEN_1.4.0.tgz vignettes: vignettes/CAEN/inst/doc/CAEN.html vignetteTitles: CAEN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAEN/inst/doc/CAEN.R dependencyCount: 26 Package: CAFE Version: 1.32.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 024276849268165cf6db5405f810c388 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: RELEASE_3_15 git_last_commit: 907bc46 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CAFE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAFE_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAFE_1.32.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 163 Package: CAGEfightR Version: 1.16.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 171ede6467563cb486a43421c2b0b44d NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: RELEASE_3_15 git_last_commit: 86ba129 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CAGEfightR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAGEfightR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAGEfightR_1.16.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow suggestsMe: nanotubes dependencyCount: 153 Package: cageminer Version: 1.2.5 Depends: R (>= 4.1) Imports: ggplot2, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: eff0275045923b24b9802c2c5016b73a NeedsCompilation: no Title: Candidate Gene Miner Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments. biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation, GeneExpression, NetworkEnrichment, VariantAnnotation, FunctionalGenomics, Network Author: Fabrício Almeida-Silva [aut, cre] (), Thiago Venancio [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cageminer VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cageminer git_url: https://git.bioconductor.org/packages/cageminer git_branch: RELEASE_3_15 git_last_commit: 101a1f4 git_last_commit_date: 2022-09-21 Date/Publication: 2022-09-22 source.ver: src/contrib/cageminer_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/cageminer_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.2/cageminer_1.2.5.tgz vignettes: vignettes/cageminer/inst/doc/cageminer.html vignetteTitles: Mining high-confidence candidate genes with cageminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cageminer/inst/doc/cageminer.R dependencyCount: 202 Package: CAGEr Version: 2.2.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, DelayedMatrixStats, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape2, rtracklayer, S4Vectors (>= 0.27.5), som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 8b2f157b3b455be99f3819fa00dc6d5e NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic [ctb], Sarvesh Nikumbh [ctb] Maintainer: Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_15 git_last_commit: 74d9a97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CAGEr_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAGEr_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAGEr_2.2.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 103 Package: calm Version: 1.10.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: a09075538f45e12681058e7616fd6298 NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: RELEASE_3_15 git_last_commit: a76133d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/calm_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/calm_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/calm_1.10.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.52.0 Depends: R (>= 3.5.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: x64 MD5sum: d967ec8245447b373ee18dc8a17ab4c1 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: RELEASE_3_15 git_last_commit: 63642b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CAMERA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAMERA_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAMERA_1.52.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 126 Package: canceR Version: 1.30.01 Depends: R (>= 4.1), tcltk Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices, R.oo, R.methodsS3, httr Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 102483ae4321f17c32cadf5faa4eb96f NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud SystemRequirements: Tktable, BWidget VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/canceR/issues git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_15 git_last_commit: 68d3ad1 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/canceR_1.30.01.tar.gz win.binary.ver: bin/windows/contrib/4.2/canceR_1.30.01.zip mac.binary.ver: bin/macosx/contrib/4.2/canceR_1.30.01.tgz vignettes: vignettes/canceR/inst/doc/canceR.html vignetteTitles: canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/canceR/inst/doc/canceR.R dependencyCount: 158 Package: cancerclass Version: 1.40.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: x64 MD5sum: 3e5e5ee7af997baf81e970c4ac2d7e77 NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: RELEASE_3_15 git_last_commit: b99f5b6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cancerclass_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cancerclass_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cancerclass_1.40.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 7 Package: CancerInSilico Version: 2.16.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl, gplots License: GPL-2 Archs: x64 MD5sum: a924ee893216d97ad908202ff2bcbbe6 NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CancerInSilico git_branch: RELEASE_3_15 git_last_commit: 84d512c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CancerInSilico_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CancerInSilico_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CancerInSilico_2.16.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R dependencyCount: 6 Package: CancerSubtypes Version: 1.22.1 Depends: R (>= 4.0), sigclust, NMF Imports: cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, knitr, RTCGA.mRNA, rmarkdown License: GPL (>= 2) MD5sum: 50ffe93071d8b6e8b58c2258a8daa4c2 NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu [aut, cre] Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues git_url: https://git.bioconductor.org/packages/CancerSubtypes git_branch: RELEASE_3_15 git_last_commit: 3245ac6 git_last_commit_date: 2022-10-16 Date/Publication: 2022-10-16 source.ver: src/contrib/CancerSubtypes_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CancerSubtypes_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CancerSubtypes_1.22.1.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R dependencyCount: 64 Package: CAnD Version: 1.27.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: a98a41b7bf3ad039f279243e677183ee NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the CAnD test on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh, Timothy Thornton Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/CAnD git_branch: master git_last_commit: e0d286b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAnD_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAnD_1.27.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAnD_1.27.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R dependencyCount: 39 Package: caOmicsV Version: 1.25.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: e1403eacb4074c719b29f722ca7f632e NeedsCompilation: no Title: Visualization of multi-dimentional cancer genomics data Description: caOmicsV package provides methods to visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout. biocViews: ImmunoOncology, Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang git_url: https://git.bioconductor.org/packages/caOmicsV git_branch: master git_last_commit: b86be55 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/caOmicsV_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/caOmicsV_1.25.0.zip mac.binary.ver: bin/macosx/contrib/4.2/caOmicsV_1.25.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R dependencyCount: 15 Package: Cardinal Version: 2.14.0 Depends: BiocGenerics, BiocParallel, EBImage, graphics, methods, S4Vectors (>= 0.27.3), stats, ProtGenerics Imports: Biobase, dplyr, irlba, lattice, Matrix, matter, magrittr, mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 6f247ed5df015da55c43574a5d6ceccb NeedsCompilation: yes Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_15 git_last_commit: 1c8f02e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Cardinal_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cardinal_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cardinal_2.14.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-2-guide.html, vignettes/Cardinal/inst/doc/Cardinal-2-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 2: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-2-guide.R, vignettes/Cardinal/inst/doc/Cardinal-2-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 64 Package: CARNIVAL Version: 2.6.2 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson, rmarkdown Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0), sessioninfo License: GPL-3 MD5sum: cc6b875b1c8c322db1e266a74830b0dd NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga [aut] (), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: RELEASE_3_15 git_last_commit: 371d5af git_last_commit_date: 2022-07-14 Date/Publication: 2022-07-14 source.ver: src/contrib/CARNIVAL_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CARNIVAL_2.6.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CARNIVAL_2.6.2.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: Contextualizing large scale signalling networks from expression footprints with CARNIVAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR suggestsMe: dce dependencyCount: 63 Package: casper Version: 2.30.0 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: x64 MD5sum: 91aa77663a59afadcf90f61ab4ef96c6 NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: RELEASE_3_15 git_last_commit: 140c9ad git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/casper_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/casper_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/casper_2.30.0.tgz vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 112 Package: CATALYST Version: 1.20.1 Depends: R (>= 4.0), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, magrittr, Matrix, matrixStats, methods, nnls, purrr, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: 0819be1ea4e7df4036ff58b15d54fe61 NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: Clustering, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry, Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: RELEASE_3_15 git_last_commit: 6296934 git_last_commit_date: 2022-05-22 Date/Publication: 2022-05-22 source.ver: src/contrib/CATALYST_1.20.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CATALYST_1.20.1.tgz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: cytofWorkflow suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 238 Package: Category Version: 2.62.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: acf047c29150009d586de670292f2da7 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: RELEASE_3_15 git_last_commit: 0fe801c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Category_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Category_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Category_2.62.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats importsMe: categoryCompare, cellHTS2, GmicR, interactiveDisplay, meshr, miRLAB, phenoTest, ppiStats, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 58 Package: categoryCompare Version: 1.40.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter, rmarkdown License: GPL-2 MD5sum: 1b9534d76fbb8b257d1cdf10b354de53 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: RELEASE_3_15 git_last_commit: b657a93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/categoryCompare_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/categoryCompare_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/categoryCompare_1.40.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 85 Package: CausalR Version: 1.28.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 005ffd41199596aee0e66f7826b1940a NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: RELEASE_3_15 git_last_commit: eb5d741 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CausalR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CausalR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CausalR_1.28.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 12 Package: cbaf Version: 1.18.4 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1a16122745e3ba477dcbe02242b98b92 NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, Microarray,ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: RELEASE_3_15 git_last_commit: f6444b1 git_last_commit_date: 2022-07-19 Date/Publication: 2022-07-21 source.ver: src/contrib/cbaf_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/cbaf_1.18.4.zip mac.binary.ver: bin/macosx/contrib/4.2/cbaf_1.18.4.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 130 Package: CBEA Version: 1.0.0 Depends: R (>= 4.2.0) Imports: BiocParallel, BiocSet, dplyr, lmom, fitdistrplus, magrittr, methods, mixtools, Rcpp (>= 1.0.7), stats, SummarizedExperiment, tibble, TreeSummarizedExperiment, tidyr, glue, generics, rlang, goftest LinkingTo: Rcpp Suggests: phyloseq, BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), tidyverse, roxygen2, mia, purrr License: MIT + file LICENSE Archs: x64 MD5sum: ddb42e882bb049b8f6348c28121142c1 NeedsCompilation: yes Title: Competitive Balances for Taxonomic Enrichment Analysis in R Description: This package implements CBEA, a method to perform set-based analysis for microbiome relative abundance data. This approach constructs a competitive balance between taxa within the set and remainder taxa per sample. More details can be found in the Nguyen et al. 2021+ manuscript. Additionally, this package adds support functions to help users perform taxa-set enrichment analyses using existing gene set analysis methods. In the future we hope to also provide curated knowledge driven taxa sets. biocViews: Software, Microbiome, Metagenomics, GeneSetEnrichment, DataImport Author: Quang Nguyen [aut, cre] () Maintainer: Quang Nguyen URL: https://github.com/qpmnguyen/CBEA, https://qpmnguyen.github.io/CBEA/ VignetteBuilder: knitr BugReports: https://github.com/qpmnguyen/CBEA//issues git_url: https://git.bioconductor.org/packages/CBEA git_branch: RELEASE_3_15 git_last_commit: 20d5b52 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CBEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CBEA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CBEA_1.0.0.tgz vignettes: vignettes/CBEA/inst/doc/basic_usage.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CBEA/inst/doc/basic_usage.R dependencyCount: 97 Package: cBioPortalData Version: 2.8.2 Depends: R (>= 4.2.0), AnVIL (>= 1.7.1), MultiAssayExperiment Imports: BiocFileCache (>= 1.5.3), digest, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 MD5sum: 92c8f62c43ee3bec21f9e2c6cd1fb0b5 NeedsCompilation: no Title: Exposes and makes available data from the cBioPortal web resources Description: The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: RELEASE_3_15 git_last_commit: 57a4caf git_last_commit_date: 2022-06-15 Date/Publication: 2022-06-16 source.ver: src/contrib/cBioPortalData_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/cBioPortalData_2.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/cBioPortalData_2.8.2.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html, vignettes/cBioPortalData/inst/doc/cgdsrMigration.html vignetteTitles: cBioPortal User Guide, cBioPortal Data Build Errors, cBioPortal Quick-start Guide, Migrating from cgdsr to cBioPortalData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R, vignettes/cBioPortalData/inst/doc/cgdsrMigration.R importsMe: cbaf, LowMACA, g3viz dependencyCount: 119 Package: cbpManager Version: 1.4.0 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 936fec46d563c76f9461518bbbe79730 NeedsCompilation: no Title: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics Description: This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients. biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI, ThirdPartyClient, Preprocessing, Visualization Author: Arsenij Ustjanzew [aut, cre, cph] (), Federico Marini [aut] () Maintainer: Arsenij Ustjanzew URL: https://arsenij-ust.github.io/cbpManager/index.html VignetteBuilder: knitr BugReports: https://github.com/arsenij-ust/cbpManager/issues git_url: https://git.bioconductor.org/packages/cbpManager git_branch: RELEASE_3_15 git_last_commit: d6c0260 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cbpManager_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cbpManager_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cbpManager_1.4.0.tgz vignettes: vignettes/cbpManager/inst/doc/intro.html vignetteTitles: intro.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cbpManager/inst/doc/intro.R dependencyCount: 85 Package: ccfindR Version: 1.16.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: e03954a5a440248855a9400067c90e59 NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: RELEASE_3_15 git_last_commit: f9758cc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ccfindR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccfindR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccfindR_1.16.0.tgz vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 38 Package: ccmap Version: 1.22.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE MD5sum: a36a95460b96618be632289e5bd83650 NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: RELEASE_3_15 git_last_commit: daaacbb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ccmap_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccmap_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccmap_1.22.0.tgz vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R dependencyCount: 59 Package: CCPROMISE Version: 1.22.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 58952c0eaea1b03085c7a864958afa60 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: RELEASE_3_15 git_last_commit: 9358ff9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CCPROMISE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CCPROMISE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CCPROMISE_1.22.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 52 Package: ccrepe Version: 1.32.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: f461cd26fe01814df194f3235bea1087 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: RELEASE_3_15 git_last_commit: 1f13e9c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ccrepe_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccrepe_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccrepe_1.32.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: celaref Version: 1.14.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: 9c6b57c2f02df3c803843d6b335d0073 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: RELEASE_3_15 git_last_commit: a0f210f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/celaref_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/celaref_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/celaref_1.14.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 78 Package: celda Version: 1.12.0 Depends: R (>= 4.0), SingleCellExperiment, Matrix Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, scater (>= 1.14.4), scran, dbscan, DelayedArray, stringr, ComplexHeatmap, multipanelfigure, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE Archs: x64 MD5sum: 53b16fea74eabfc37a70c1d2eb30a246 NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut], Sean Corbett [aut], Yusuke Koga [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: RELEASE_3_15 git_last_commit: 366a243 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/celda_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/celda_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/celda_1.12.0.tgz vignettes: vignettes/celda/inst/doc/celda.html, vignettes/celda/inst/doc/decontX.html vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: singleCellTK dependencyCount: 136 Package: CellaRepertorium Version: 1.6.0 Depends: R (>= 4.0) Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods, rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr, forcats, progress, stats, utils LinkingTo: Rcpp Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle, ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment, scater, broom.mixed, cowplot, igraph, ggraph License: GPL-3 Archs: x64 MD5sum: 85d73faa72868a91b0d4660d7c1ed9f3 NeedsCompilation: yes Title: Data structures, clustering and testing for single cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq) Description: Methods to cluster and analyze high-throughput single cell immune cell repertoires, especially from the 10X Genomics VDJ solution. Contains an R interface to CD-HIT (Li and Godzik 2006). Methods to visualize and analyze paired heavy-light chain data. Tests for specific expansion, as well as omnibus oligoclonality under hypergeometric models. biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing, Technology, ImmunoOncology, Clustering Author: Andrew McDavid [aut, cre], Yu Gu [aut], Erik VonKaenel [aut], Aaron Wagner [aut], Thomas Lin Pedersen [ctb] Maintainer: Andrew McDavid URL: https://github.com/amcdavid/CellaRepertorium VignetteBuilder: knitr BugReports: https://github.com/amcdavid/CellaRepertorium/issues git_url: https://git.bioconductor.org/packages/CellaRepertorium git_branch: RELEASE_3_15 git_last_commit: 5cf49a5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellaRepertorium_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellaRepertorium_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellaRepertorium_1.6.0.tgz vignettes: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.html, vignettes/CellaRepertorium/inst/doc/cr-overview.html, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.html, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.html vignetteTitles: Clustering and differential usage of repertoire CDR3 sequences, An Introduction to CellaRepertorium, Quality control and Exploration of UMI-based repertoire data, Combining Repertoire with Expression with SingleCellExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.R, vignettes/CellaRepertorium/inst/doc/cr-overview.R, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.R, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.R dependencyCount: 50 Package: CellBarcode Version: 1.2.0 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 1578282680b36fb083ddbc92c4037ea3 NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: This package performs Cellular DNA Barcode (genetic lineage tracing) analysis. The package can handle all kinds of DNA barcodes, as long as the barcode within a single sequencing read and has a pattern which can be matched by a regular expression. This package can handle barcode with flexible length, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing of some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing and meta genome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre], Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: RELEASE_3_15 git_last_commit: bdde81f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellBarcode_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellBarcode_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellBarcode_1.2.0.tgz vignettes: vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 85 Package: cellbaseR Version: 1.20.1 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: e081fe64971387b9216b14d0b02fe8b6 NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: RELEASE_3_15 git_last_commit: 2f86a12 git_last_commit_date: 2022-06-21 Date/Publication: 2022-06-23 source.ver: src/contrib/cellbaseR_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellbaseR_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cellbaseR_1.20.1.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 65 Package: CellBench Version: 1.12.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: 85acd85718423a494dcbec44182d41cf NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: RELEASE_3_15 git_last_commit: 08bd832 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellBench_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellBench_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellBench_1.12.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf, vignettes/CellBench/inst/doc/TidyversePatterns.pdf, vignettes/CellBench/inst/doc/CellBenchCaseStudy.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Tidyverse Patterns, CellBenchCaseStudy.html, Introduction, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral dependencyCount: 78 Package: cellHTS2 Version: 2.60.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: 86088bfba60427a759e2a8d4365896f1 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber git_url: https://git.bioconductor.org/packages/cellHTS2 git_branch: RELEASE_3_15 git_last_commit: 32edd2a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellHTS2_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellHTS2_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellHTS2_2.60.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, RNAinteract suggestsMe: bioassayR dependencyCount: 89 Package: CelliD Version: 1.4.0 Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater, stringr, irlba, data.table, glue, pbapply, umap, Rtsne, reticulate, fastmatch, matrixStats, ggplot2, BiocParallel, SummarizedExperiment, fgsea LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr, destiny, ggrepel License: GPL-3 + file LICENSE Archs: x64 MD5sum: 5bdbd3f45f49a3fb0f6912d81e19ab57 NeedsCompilation: yes Title: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis Description: CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data. biocViews: RNASeq, SingleCell, DimensionReduction, Clustering, GeneSetEnrichment, GeneExpression, ATACSeq Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb] Maintainer: Akira Cortal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CelliD git_branch: RELEASE_3_15 git_last_commit: 743850b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CelliD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CelliD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CelliD_1.4.0.tgz vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html vignetteTitles: CelliD Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R dependencyCount: 187 Package: cellity Version: 1.24.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: c78ad2bd863bd4317776beb245263931 NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: RELEASE_3_15 git_last_commit: 35aaa63 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellity_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellity_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellity_1.24.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 84 Package: CellMapper Version: 1.22.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 9aa4e6a15734b4ba50720ef190e29663 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: RELEASE_3_15 git_last_commit: 7668211 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellMapper_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellMapper_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellMapper_1.22.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 7 Package: cellmigRation Version: 1.4.0 Depends: R (>= 4.1), methods, foreach Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel, grDevices, matrixStats, FME, SpatialTools, sp, vioplot, FactoMineR, Hmisc Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics, BiocManager, kableExtra, rgl License: GPL-2 MD5sum: 12621bdc3cdceab6932dd1d2f15a9c92 NeedsCompilation: no Title: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics Description: Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions. biocViews: CellBiology, DataRepresentation, DataImport Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir Leoncio [cre, aut], Øystein Sørensen [aut] Maintainer: Waldir Leoncio URL: https://github.com/ocbe-uio/cellmigRation/ VignetteBuilder: knitr BugReports: https://github.com/ocbe-uio/cellmigRation/issues git_url: https://git.bioconductor.org/packages/cellmigRation git_branch: RELEASE_3_15 git_last_commit: c95600f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellmigRation_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellmigRation_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellmigRation_1.4.0.tgz vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html vignetteTitles: cellmigRation hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R dependencyCount: 144 Package: CellMixS Version: 1.12.0 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: 12ee1fe06474cd8f36c1116a3e4a7280 NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: RELEASE_3_15 git_last_commit: ab74242 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellMixS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellMixS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellMixS_1.12.0.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 95 Package: CellNOptR Version: 1.42.1 Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML, ggplot2, rmarkdown Imports: igraph, stringi, stringr Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics, Enhances: doParallel, foreach License: GPL-3 Archs: x64 MD5sum: 7ea5f3f5f0af9c7abcbadb607b45c948 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara [aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_15 git_last_commit: 6b76f2d git_last_commit_date: 2022-06-14 Date/Publication: 2022-06-14 source.ver: src/contrib/CellNOptR_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellNOptR_1.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CellNOptR_1.42.1.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html vignetteTitles: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode importsMe: bnem suggestsMe: MEIGOR dependencyCount: 67 Package: cellscape Version: 1.20.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 21495263cd3df1ed08fb4c0b03e272be NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_15 git_last_commit: a3d37b0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellscape_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellscape_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellscape_1.20.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 35 Package: CellScore Version: 1.16.0 Depends: R (>= 3.5.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: 88ff77bc3cbdfc1caaad6e533356ff3b NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: RELEASE_3_15 git_last_commit: 03f83cd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellScore_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellScore_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellScore_1.16.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R dependencyCount: 16 Package: CellTrails Version: 1.14.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: d242c01059b6be0381131925a9c3e310 NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: RELEASE_3_15 git_last_commit: 3ad6bc4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CellTrails_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellTrails_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellTrails_1.14.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 74 Package: cellTree Version: 1.26.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: 48543284bc80468cc6cf5b7abcfe072e NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellTree git_branch: RELEASE_3_15 git_last_commit: b234c33 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellTree_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellTree_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellTree_1.26.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R dependencyCount: 69 Package: cellxgenedp Version: 1.0.1 Imports: dplyr, httr, curl, jsonlite, utils, tools, parallel, shiny, DT LinkingTo: cpp11 Suggests: zellkonverter, SingleCellExperiment, HDF5Array, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 | BSL-1.0 | file LICENSE Archs: x64 MD5sum: fc33b90e76b614aa8da25750a82175c4 NeedsCompilation: yes Title: Discover and Access Single Cell Data Sets in the cellxgene Data Portal Description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading. biocViews: SingleCell, DataImport, ThirdPartyClient Author: Martin Morgan [aut, cre] (), Kayla Interdonato [aut], Daniel Parker [aut, cph] (jsoncons C++ library creator) Maintainer: Martin Morgan SystemRequirements: C++14 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellxgenedp git_branch: RELEASE_3_15 git_last_commit: d1e1445 git_last_commit_date: 2022-08-22 Date/Publication: 2022-08-23 source.ver: src/contrib/cellxgenedp_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellxgenedp_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cellxgenedp_1.0.1.tgz vignettes: vignettes/cellxgenedp/inst/doc/using_cellxgenedp.html vignetteTitles: Discover and download datasets and files from the cellxgene data portal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cellxgenedp/inst/doc/using_cellxgenedp.R importsMe: UniProt.ws dependencyCount: 58 Package: CEMiTool Version: 1.20.0 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: c978bcdb89c7bb535cacb00ef476e108 NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_15 git_last_commit: 4d93882 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CEMiTool_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CEMiTool_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CEMiTool_1.20.0.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 191 Package: censcyt Version: 1.4.0 Depends: R (>= 4.0), diffcyt Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR, fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp, purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment, survival, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2 License: MIT + file LICENSE MD5sum: 1bf31d89977b1fbf44c2fc83c12dc424 NeedsCompilation: no Title: Differential abundance analysis with a right censored covariate in high-dimensional cytometry Description: Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software, Survival Author: Reto Gerber [aut, cre] () Maintainer: Reto Gerber URL: https://github.com/retogerber/censcyt VignetteBuilder: knitr BugReports: https://github.com/retogerber/censcyt/issues git_url: https://git.bioconductor.org/packages/censcyt git_branch: RELEASE_3_15 git_last_commit: 1545e87 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/censcyt_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/censcyt_1.4.0.tgz vignettes: vignettes/censcyt/inst/doc/censored_covariate.html vignetteTitles: Censored covariate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R dependencyCount: 231 Package: Cepo Version: 1.2.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap, patchwork License: MIT + file LICENSE MD5sum: 5ea98388724d59bf315eae5a69da5f87 NeedsCompilation: no Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (), Kevin Wang [aut] () Maintainer: Hani Jieun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cepo git_branch: RELEASE_3_15 git_last_commit: 5c76000 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Cepo_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cepo_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cepo_1.2.0.tgz vignettes: vignettes/Cepo/inst/doc/cepo.html vignetteTitles: Cepo method for differential stability analysis of scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cepo/inst/doc/cepo.R importsMe: scClassify dependencyCount: 104 Package: ceRNAnetsim Version: 1.8.0 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) MD5sum: e335594a22747770ca0edf46e3e179c8 NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (), Alper Yilmaz [aut] () Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: RELEASE_3_15 git_last_commit: cb85212 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ceRNAnetsim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ceRNAnetsim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ceRNAnetsim_1.8.0.tgz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 65 Package: CeTF Version: 1.8.0 Depends: R (>= 4.0), methods Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 804ee8a1457dcd60236b48422941c747 NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev, gfortran, build-essential, libz-dev, zlib1g-dev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: RELEASE_3_15 git_last_commit: 5503245 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CeTF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CeTF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CeTF_1.8.0.tgz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 220 Package: CexoR Version: 1.34.0 Depends: R (>= 4.0.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 574504c3d6e4c281599d2d2224a02641 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed. biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq, PeakDetection Author: Pedro Madrigal [aut, cre] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: RELEASE_3_15 git_last_commit: c90524a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CexoR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CexoR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CexoR_1.34.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 99 Package: CFAssay Version: 1.30.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 9149e78434007f7fe30a94e77dfa9e2d NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: RELEASE_3_15 git_last_commit: 099f5a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CFAssay_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CFAssay_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CFAssay_1.30.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfDNAPro Version: 1.2.0 Depends: R (>= 4.0), magrittr (>= 1.5.0), Imports: stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0) Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: 898fb3dc19939767eb3b00b8b64bad75 NeedsCompilation: no Title: cfDNAPro Helps Characterise and Visualise Whole Genome Sequencing Data from Liquid Biopsy Description: cfDNA fragment size metrics are important features for utilizing liquid biopsy in tumor early detection, diagnosis, therapy personlization and monitoring. Analyzing and visualizing insert size metrics could be time intensive. This package intends to simplify this exploration process, and it offers two sets of functions for data characterization and data visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Nitzan Rosenfeld [ctb], Hui Zhao [ctb], Christopher Smith [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: RELEASE_3_15 git_last_commit: 01b93a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cfDNAPro_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cfDNAPro_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cfDNAPro_1.2.0.tgz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 71 Package: CGEN Version: 3.32.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE Archs: x64 MD5sum: 2a60329e17977e2c877d268682c28550 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population. biocViews: SNP, MultipleComparison, Clustering Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut], Summer Han [aut], Minsun Song [aut], William Wheeler [aut], Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin Lee [cre] Maintainer: Justin Lee git_url: https://git.bioconductor.org/packages/CGEN git_branch: RELEASE_3_15 git_last_commit: 071dd23 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CGEN_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGEN_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGEN_3.32.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.56.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 061a57ae426ff8b2a888b64806c8e487 NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: RELEASE_3_15 git_last_commit: a5a82a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CGHbase_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHbase_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHbase_1.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq, ragt2ridges dependencyCount: 9 Package: CGHcall Version: 2.58.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 83a68c7aec837af94584a7ba9e645adf NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: RELEASE_3_15 git_last_commit: 483edc7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CGHcall_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHcall_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHcall_2.58.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 14 Package: cghMCR Version: 1.54.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 1ab989ee757aca67537551257807f670 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: RELEASE_3_15 git_last_commit: bd66110 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cghMCR_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cghMCR_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cghMCR_1.54.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 57 Package: CGHnormaliter Version: 1.50.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 18b2864d3a7c6ee171428f8b43cbe1db NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: RELEASE_3_15 git_last_commit: 39abab6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CGHnormaliter_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHnormaliter_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHnormaliter_1.50.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 15 Package: CGHregions Version: 1.54.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: e70c0fe5dd87419d5db20ceaa048090f NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: RELEASE_3_15 git_last_commit: ee2fb77 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CGHregions_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHregions_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHregions_1.54.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 10 Package: ChAMP Version: 2.26.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 069faf7423608c4982a156ac1a5247b8 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_15 git_last_commit: 1548910 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChAMP_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChAMP_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChAMP_2.26.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R suggestsMe: GeoTcgaData dependencyCount: 256 Package: ChemmineOB Version: 1.34.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp, zlibbioc Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: x64 MD5sum: 2ada94fe150ceb108cbf5ca1d74b8346 NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: RELEASE_3_15 git_last_commit: 4808acd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChemmineOB_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChemmineOB_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChemmineOB_1.34.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R dependencyCount: 8 Package: ChemmineR Version: 3.48.0 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg,jsonlite,stringi LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager Enhances: ChemmineOB License: Artistic-2.0 Archs: x64 MD5sum: 2c20a3fb1fd0a02f1767e43be4e3683d NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: RELEASE_3_15 git_last_commit: 6eb6f02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChemmineR_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChemmineR_3.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChemmineR_3.48.0.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID, chemodiv, DRviaSPCN, uCAREChemSuiteCLI suggestsMe: ChemmineOB, xnet dependencyCount: 60 Package: CHETAH Version: 1.12.1 Depends: R (>= 4.2), ggplot2, SingleCellExperiment Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE MD5sum: 286c0c5e591e004e79530ef6cb3fd2f4 NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering, GeneExpression, ImmunoOncology Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: RELEASE_3_15 git_last_commit: 3dd83d6 git_last_commit_date: 2022-07-08 Date/Publication: 2022-07-12 source.ver: src/contrib/CHETAH_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CHETAH_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CHETAH_1.12.1.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R dependencyCount: 107 Package: ChIC Version: 1.16.0 Depends: spp, R (>= 3.6) Imports: ChIC.data (>= 1.11.1), caTools, methods, GenomicRanges, IRanges, parallel, progress, randomForest, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics, genomeIntervals, Rsamtools License: GPL-2 MD5sum: 8aa8ce07c2fe68e6caaba6c9a3107883 NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control (QC) pipeline for ChIP-seq data using a comprehensive set of QC metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The package provides functions to calculate a set of QC metrics, a compendium with reference values and machine learning models to score sample quality. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi git_url: https://git.bioconductor.org/packages/ChIC git_branch: RELEASE_3_15 git_last_commit: 6904ed2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIC_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ChIC_1.16.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R dependencyCount: 111 Package: Chicago Version: 1.24.0 Depends: R (>= 3.3.1), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: babb40c8b854d4a20c0a36273e932f70 NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: RELEASE_3_15 git_last_commit: 0f27478 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Chicago_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Chicago_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Chicago_1.24.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 72 Package: chimeraviz Version: 1.22.0 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, checkmate, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 5a939299bf847ff4c14b3516ef4b6ea0 NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_15 git_last_commit: 6426ef0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chimeraviz_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chimeraviz_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chimeraviz_1.22.0.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 167 Package: ChIPanalyser Version: 1.18.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 6206bf6fc2d7aa9eecf3bc9e8e807586 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: RELEASE_3_15 git_last_commit: 4bc63a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPanalyser_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPanalyser_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPanalyser_1.18.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R dependencyCount: 54 Package: ChIPComp Version: 1.26.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL Archs: x64 MD5sum: 683c78aa338e80925d931bfa15495e14 NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: RELEASE_3_15 git_last_commit: 7449627 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPComp_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPComp_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPComp_1.26.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 49 Package: chipenrich Version: 2.20.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: d59eca3189212d67d2adcb0a440dcaa4 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_15 git_last_commit: ed01530 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chipenrich_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chipenrich_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chipenrich_2.20.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 148 Package: ChIPexoQual Version: 1.20.0 Depends: R (>= 3.5.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: b346906d67baa1ce1b97a3a5ade909ec NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: RELEASE_3_15 git_last_commit: d4cc573 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPexoQual_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPexoQual_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPexoQual_1.20.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 155 Package: ChIPpeakAnno Version: 3.30.1 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), DBI, dplyr, ensembldb, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: 8a1855d16d021fdb471d43fc6af24b5a NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_15 git_last_commit: f358fc5 git_last_commit_date: 2022-06-06 Date/Publication: 2022-06-07 source.ver: src/contrib/ChIPpeakAnno_3.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPpeakAnno_3.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPpeakAnno_3.30.1.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq, csawBook importsMe: ATACseqQC, DEScan2, GUIDEseq suggestsMe: R3CPET, seqsetvis, chipseqDB dependencyCount: 123 Package: ChIPQC Version: 1.32.2 Depends: R (>= 3.5.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19), BiocParallel Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: de73075233003ab172d9bdcd794f0b32 NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: RELEASE_3_15 git_last_commit: 6f49921 git_last_commit_date: 2022-08-08 Date/Publication: 2022-08-09 source.ver: src/contrib/ChIPQC_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPQC_1.32.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPQC_1.32.2.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R dependencyCount: 166 Package: ChIPseeker Version: 1.32.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, ggVennDiagram, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, tibble License: Artistic-2.0 MD5sum: 4eee502d97a8f2b091d48ce14b2f8948 NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (), Ming Li [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_15 git_last_commit: 61077e6 git_last_commit_date: 2022-09-15 Date/Publication: 2022-09-15 source.ver: src/contrib/ChIPseeker_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPseeker_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPseeker_1.32.1.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: EpiCompare, esATAC, segmenter, TCGAWorkflow, cinaR suggestsMe: GRaNIE, curatedAdipoChIP dependencyCount: 178 Package: chipseq Version: 1.46.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: x64 MD5sum: b6b2a23a62d63f1532f09ca810f4c405 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_15 git_last_commit: 76b0039 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chipseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chipseq_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chipseq_1.46.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR dependencyCount: 50 Package: ChIPseqR Version: 1.50.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: x64 MD5sum: 213e794285e538b502f9def77a131cb7 NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: RELEASE_3_15 git_last_commit: fed1a68 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPseqR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPseqR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPseqR_1.50.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 58 Package: ChIPsim Version: 1.50.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: a18f9defec290f2a51034d41c8d89b96 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: RELEASE_3_15 git_last_commit: 3f104cd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPsim_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPsim_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPsim_1.50.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 50 Package: ChIPXpress Version: 1.40.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 59086645cce1c5dafc10750fe293e5c7 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: RELEASE_3_15 git_last_commit: b47eccb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChIPXpress_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ChIPXpress_1.40.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 93 Package: chopsticks Version: 1.62.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: 1b4d87dbf2c544df7579b08ebcb4dda9 NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: RELEASE_3_15 git_last_commit: 35d56cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chopsticks_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chopsticks_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chopsticks_1.62.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R importsMe: CrypticIBDcheck, rJPSGCS dependencyCount: 10 Package: chromDraw Version: 2.26.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: x64 MD5sum: 0c09572da717cdd6f72d2e1abfbadf9d NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: RELEASE_3_15 git_last_commit: eca1bd0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chromDraw_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromDraw_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromDraw_2.26.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 17 Package: ChromHeatMap Version: 1.50.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: ea05df514e682b410b98226784aa80f8 NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: RELEASE_3_15 git_last_commit: c63800c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChromHeatMap_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChromHeatMap_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChromHeatMap_1.50.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 73 Package: chromPlot Version: 1.24.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: af97670a67dd011a6a3ded644f38b8b5 NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: RELEASE_3_15 git_last_commit: 5aa55cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chromPlot_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromPlot_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromPlot_1.24.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 74 Package: ChromSCape Version: 1.6.0 Depends: R (>= 4.1) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, ggrepel, gggenes, gridExtra, qualV, stringdist, fs, qs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future, igraph, bluster, httr License: GPL-3 Archs: x64 MD5sum: 0e6784c9a4983711b286fc5fbc324eea NeedsCompilation: yes Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (), Celine Vallot [aut] () Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: RELEASE_3_15 git_last_commit: f0f1527 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ChromSCape_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChromSCape_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChromSCape_1.6.0.tgz vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 204 Package: chromstaR Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 Archs: x64 MD5sum: e36e52d76ee9aed5bf718eebfd21b81d NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: ImmunoOncology, Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: RELEASE_3_15 git_last_commit: 5f6ba7e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chromstaR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromstaR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromstaR_1.22.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R dependencyCount: 78 Package: chromswitch Version: 1.18.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE MD5sum: 62b038fb31e9ded54cceaf82442bcaa1 NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: ImmunoOncology, MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues git_url: https://git.bioconductor.org/packages/chromswitch git_branch: RELEASE_3_15 git_last_commit: 38d1fce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chromswitch_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromswitch_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromswitch_1.18.0.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R dependencyCount: 102 Package: chromVAR Version: 1.18.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE Archs: x64 MD5sum: 0b672471e087a767d2e8b841b14d0311 NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: RELEASE_3_15 git_last_commit: 42d22d3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/chromVAR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromVAR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromVAR_1.18.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R suggestsMe: Signac dependencyCount: 152 Package: CHRONOS Version: 1.24.1 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 57a54c43d0d82d85fc59e30af195ba7c NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_15 git_last_commit: 9dcd2f9 git_last_commit_date: 2022-06-09 Date/Publication: 2022-06-12 source.ver: src/contrib/CHRONOS_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CHRONOS_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CHRONOS_1.24.1.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 90 Package: cicero Version: 1.14.0 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: 4669a563627089fedb627e564f8e0000 NeedsCompilation: no Title: Predict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: RELEASE_3_15 git_last_commit: 40708f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cicero_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cicero_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cicero_1.14.0.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R dependencyCount: 175 Package: CIMICE Version: 1.4.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat, tidygraph, expm, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 MD5sum: c9f0a61405d70c9d4a306155632d8d7b NeedsCompilation: no Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution Description: CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction. biocViews: Software, BiologicalQuestion, NetworkInference, ResearchField, Phylogenetics, StatisticalMethod, GraphAndNetwork, Technology, SingleCell Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and Bioinformatics, Department of Mathematics, Computer Science and Physics, University of Udine, ) Maintainer: Nicolò Rossi URL: https://github.com/redsnic/CIMICE VignetteBuilder: knitr BugReports: https://github.com/redsnic/CIMICE/issues git_url: https://git.bioconductor.org/packages/CIMICE git_branch: RELEASE_3_15 git_last_commit: 474cda8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CIMICE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CIMICE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CIMICE_1.4.0.tgz vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html, vignettes/CIMICE/inst/doc/CIMICER.html vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer Cancer Evolution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R, vignettes/CIMICE/inst/doc/CIMICER.R dependencyCount: 79 Package: CINdex Version: 1.24.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens, R.utils License: GPL (>= 2) MD5sum: d0ea00ba82f6490a429ac8fd2e609734 NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Krithika Bhuvaneshwar [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yue Wang [aut, ths] (Virginia Polytechnic Institute and State University), Yuanjian Feng [aut] (Virginia Polytechnic Institute and State University), Ie-Ming Shih [aut] (Johns Hopkins University School of Medicine), Subha Madhavan [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yuriy Gusev [aut, cre] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center) Maintainer: Yuriy Gusev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: RELEASE_3_15 git_last_commit: 269715a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CINdex_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CINdex_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CINdex_1.24.0.tgz vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, How to obtain Cytoband and Stain Information, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.R, vignettes/CINdex/inst/doc/PrepareInputData.R dependencyCount: 44 Package: circRNAprofiler Version: 1.10.0 Depends: R(>= 4.1.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: 5cf12041ddb2aabc3bf176799519f9fc NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: RELEASE_3_15 git_last_commit: 04b7e52 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/circRNAprofiler_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/circRNAprofiler_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/circRNAprofiler_1.10.0.tgz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 164 Package: cisPath Version: 1.36.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: x64 MD5sum: 0491a58d12bd9a843b2c6cc07553d4c6 NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: RELEASE_3_15 git_last_commit: 8db76db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cisPath_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cisPath_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cisPath_1.36.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R dependencyCount: 2 Package: CiteFuse Version: 1.8.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, propr, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 5a2cf40c68d65b65b0a6f261f7884245 NeedsCompilation: no Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: RELEASE_3_15 git_last_commit: 75bb83a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CiteFuse_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CiteFuse_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CiteFuse_1.8.0.tgz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R suggestsMe: MuData dependencyCount: 127 Package: ClassifyR Version: 3.0.3 Depends: R (>= 4.1.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel, survival Imports: grid, utils, dplyr, tidyr, rlang, randomForest Suggests: limma, genefilter, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0), gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class License: GPL-3 MD5sum: 7e30e65f7e717d8f217ccd43008afc2c NeedsCompilation: no Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac, Ellis Patrick, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_15 git_last_commit: 1d0f471 git_last_commit_date: 2022-05-11 Date/Publication: 2022-05-15 source.ver: src/contrib/ClassifyR_3.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClassifyR_3.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ClassifyR_3.0.3.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/wrapper.html vignetteTitles: An Introduction to the ClassifyR Package, Example: Creating a Wrapper Function for the k-NN Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/wrapper.R dependencyCount: 58 Package: cleanUpdTSeq Version: 1.34.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, GenomeInfoDb, IRanges, utils, stringr, stats, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: 953c3f1a271337d76bd9ce686c4c00da NeedsCompilation: no Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data Description: This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods. biocViews: Sequencing, 3' end sequencing, polyadenylation site, internal priming Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: RELEASE_3_15 git_last_commit: 1efc876 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cleanUpdTSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cleanUpdTSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cleanUpdTSeq_1.34.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R dependencyCount: 61 Package: cleaver Version: 1.34.1 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.36.5) License: GPL (>= 3) MD5sum: 0aa871103b0cf37bf7e739823b4eb3ae NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: RELEASE_3_15 git_last_commit: 7f683e8 git_last_commit_date: 2022-08-31 Date/Publication: 2022-09-01 source.ver: src/contrib/cleaver_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cleaver_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cleaver_1.34.1.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: ProteoDisco, synapter suggestsMe: RforProteomics dependencyCount: 18 Package: clippda Version: 1.46.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 566ca5b8e68033430d84d66a19e02f30 NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: RELEASE_3_15 git_last_commit: deadf01 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clippda_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clippda_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clippda_1.46.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 32 Package: clipper Version: 1.36.1 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: 0aa14b8810923194f911761cd9b72b68 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: RELEASE_3_15 git_last_commit: d326ac8 git_last_commit_date: 2022-09-21 Date/Publication: 2022-09-22 source.ver: src/contrib/clipper_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/clipper_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.2/clipper_1.36.1.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite, simPATHy dependencyCount: 112 Package: cliProfiler Version: 1.2.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 6fae0e6a876bb2722542630690c3f1b9 NeedsCompilation: no Title: A package for the CLIP data visualization Description: An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results. biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics Author: You Zhou [aut, cre] (), Kathi Zarnack [aut] () Maintainer: You Zhou URL: https://github.com/Codezy99/cliProfiler VignetteBuilder: knitr BugReports: https://github.com/Codezy99/cliProfiler/issues git_url: https://git.bioconductor.org/packages/cliProfiler git_branch: RELEASE_3_15 git_last_commit: 4bb48bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cliProfiler_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cliProfiler_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cliProfiler_1.2.0.tgz vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html vignetteTitles: cliProfiler Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R dependencyCount: 77 Package: cliqueMS Version: 1.10.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) Archs: x64 MD5sum: cef48286f880cfd3a762fc9450712dcc NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_15 git_last_commit: f846160 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cliqueMS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cliqueMS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cliqueMS_1.10.0.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 98 Package: Clomial Version: 1.32.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 797f3698aa06d7ba87316c991e404c3a NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: RELEASE_3_15 git_last_commit: 953a2a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Clomial_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Clomial_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Clomial_1.32.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: Clonality Version: 1.44.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: 0d06e9107966ff57670f17401d65538d NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis, metastasis Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya git_url: https://git.bioconductor.org/packages/Clonality git_branch: RELEASE_3_15 git_last_commit: b83a4a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Clonality_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Clonality_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Clonality_1.44.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R dependencyCount: 5 Package: clonotypeR Version: 1.34.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, rmarkdown, RUnit, vegan License: file LICENSE MD5sum: 0625c69525b2f717ae6ef8153b43a0c4 NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy URL: http://clonotyper.branchable.com/ VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/clonotypeR git_branch: RELEASE_3_15 git_last_commit: 075ab7a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clonotypeR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clonotypeR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clonotypeR_1.34.0.tgz vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html vignetteTitles: clonotypeR User's Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R dependencyCount: 1 Package: clst Version: 1.44.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 77966cc2c04b8fda7556894f08d744a4 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: RELEASE_3_15 git_last_commit: b0df5f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clst_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clst_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clst_1.44.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 18 Package: clstutils Version: 1.44.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 2b46c488718911bdb3030a7fd068c46c NeedsCompilation: no Title: Tools for performing taxonomic assignment Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: RELEASE_3_15 git_last_commit: f9d7f81 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clstutils_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clstutils_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clstutils_1.44.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 37 Package: CluMSID Version: 1.12.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE MD5sum: 53f437d6c0d5ee8ad28a8ef24fd65171 NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: RELEASE_3_15 git_last_commit: d40d5dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CluMSID_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CluMSID_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CluMSID_1.12.0.tgz vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 119 Package: clustComp Version: 1.24.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: df9f458b6a1d81d4e43d176b45fffbf0 NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: RELEASE_3_15 git_last_commit: 1656d13 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clustComp_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clustComp_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clustComp_1.24.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.16.0 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, howmany, locfdr, matrixStats, graphics, parallel, BiocSingular, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 1bcaca88e1476e53ad4ee3f1a2443b82 NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: RELEASE_3_15 git_last_commit: c3f6d8e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clusterExperiment_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterExperiment_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterExperiment_2.16.0.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html, vignettes/clusterExperiment/inst/doc/largeDataSets.html vignetteTitles: clusterExperiment Vignette, Working with Large Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R, vignettes/clusterExperiment/inst/doc/largeDataSets.R dependsOnMe: netSmooth suggestsMe: netDx, slingshot, tradeSeq dependencyCount: 151 Package: ClusterJudge Version: 1.18.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: b68315f4f5b8b02d2c2a2a790006c36c NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: RELEASE_3_15 git_last_commit: 2081ad9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ClusterJudge_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClusterJudge_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ClusterJudge_1.18.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 27 Package: clusterProfiler Version: 4.4.4 Depends: R (>= 3.5.0) Imports: AnnotationDbi, downloader, DOSE (>= 3.13.1), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim, magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils Suggests: AnnotationHub, knitr, rmarkdown, org.Hs.eg.db, prettydoc, ReactomePA, testthat License: Artistic-2.0 MD5sum: 3389e873323adeaedd6b04a99359050c NeedsCompilation: no Title: A universal enrichment tool for interpreting omics data Description: This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Li-Gen Wang [ctb], Erqiang Hu [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ (docs), https://doi.org/10.1016/j.xinn.2021.100141 (paper) VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_15 git_last_commit: 9fca9a4 git_last_commit_date: 2022-06-20 Date/Publication: 2022-06-21 source.ver: src/contrib/clusterProfiler_4.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterProfiler_4.4.4.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterProfiler_4.4.4.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R dependsOnMe: maEndToEnd importsMe: bioCancer, CEMiTool, CeTF, conclus, debrowser, eegc, enrichTF, EpiCompare, esATAC, famat, fcoex, GDCRNATools, IRISFGM, MAGeCKFlute, methylGSA, MicrobiomeProfiler, miRspongeR, MoonlightR, multiSight, netboxr, PanomiR, PFP, Pigengene, signatureSearch, TCGAbiolinksGUI, TimiRGeN, ExpHunterSuite, recountWorkflow, TCGAWorkflow, DRviaSPCN, genekitr, immcp, pathwayTMB, PMAPscore, RVA, tinyarray suggestsMe: ChIPseeker, cola, DAPAR, DOSE, enrichplot, epihet, GeneTonic, GenomicSuperSignature, GOSemSim, GRaNIE, GSEAmining, MesKit, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, org.Mxanthus.db, cRegulome, GeoTcgaData, grandR, OlinkAnalyze dependencyCount: 126 Package: clusterSeq Version: 1.20.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: ded276a872ff808a7b843bf851d3d5a0 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle & Irene Papatheodorou Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_15 git_last_commit: 171d4a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clusterSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterSeq_1.20.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 34 Package: ClusterSignificance Version: 1.24.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: c9adad094ea399c6f57671c61508aab7 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_15 git_last_commit: 2683a3f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ClusterSignificance_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClusterSignificance_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ClusterSignificance_1.24.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.68.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: d19de5aefb9b88e5d0531aac424d58d9 NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: RELEASE_3_15 git_last_commit: 8cb667b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clusterStab_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterStab_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterStab_1.68.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 6 Package: clustifyr Version: 1.8.0 Depends: R (>= 4.0) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, readr, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr License: MIT + file LICENSE MD5sum: bf237ab0393414d4e6b33cf3ef88e6b3 NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray, GeneExpression Author: Rui Fu [aut], Kent Riemondy [cre, aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd] Maintainer: Kent Riemondy URL: http://github.com/rnabioco/clustifyr#readme, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: RELEASE_3_15 git_last_commit: 0aa93bc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/clustifyr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clustifyr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clustifyr_1.8.0.tgz vignettes: vignettes/clustifyr/inst/doc/clustifyR.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyR.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 97 Package: CMA Version: 1.54.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: dc6f903ec1369d7c86ba99ab0cb2626f NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: RELEASE_3_15 git_last_commit: 0745710 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CMA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CMA_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CMA_1.54.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 6 Package: cmapR Version: 1.8.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: a90f5afd619e4d161903626903f1772d NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] () Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: RELEASE_3_15 git_last_commit: 83f68b6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cmapR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cmapR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cmapR_1.8.0.tgz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 36 Package: cn.farms Version: 1.44.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: x64 MD5sum: e274d15a6eb3ff13cdeef4d37fbae634 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: RELEASE_3_15 git_last_commit: b002089 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cn.farms_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cn.farms_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cn.farms_1.44.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 55 Package: cn.mops Version: 1.42.0 Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: x64 MD5sum: fadea8757f1454fa33d3c9ede115f7e3 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer [aut], Gundula Povysil [cre] Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: RELEASE_3_15 git_last_commit: 64e5afb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cn.mops_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cn.mops_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cn.mops_1.42.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots suggestsMe: CNVgears dependencyCount: 32 Package: CNAnorm Version: 1.42.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: x64 MD5sum: b6216611c22ab458987d8971c17c4c70 NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: RELEASE_3_15 git_last_commit: 4ad9fe7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNAnorm_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNAnorm_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNAnorm_1.42.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.32.0 Depends: R (>= 3.5.0) Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: x64 MD5sum: e7284235e2bdde3a0de38d6bc72e3645 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: RELEASE_3_15 git_last_commit: 1c92f3d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNEr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNEr_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNEr_1.32.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools dependencyCount: 115 Package: CNORdt Version: 1.38.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: x64 MD5sum: a1a1d59eaafd664cf770ff153a287996 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: RELEASE_3_15 git_last_commit: 3b2c903 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNORdt_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORdt_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORdt_1.38.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 69 Package: CNORfeeder Version: 1.36.1 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: b172d524336494bdf3d4feb54f1bd2f2 NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: Federica Eduati [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_15 git_last_commit: 1205add git_last_commit_date: 2022-05-16 Date/Publication: 2022-05-17 source.ver: src/contrib/CNORfeeder_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORfeeder_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORfeeder_1.36.1.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 68 Package: CNORfuzzy Version: 1.38.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 0a15e7d18fb85b737c39828bee37eeb9 NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: RELEASE_3_15 git_last_commit: 84dbec1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNORfuzzy_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORfuzzy_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORfuzzy_1.38.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 83 Package: CNORode Version: 1.38.0 Depends: CellNOptR, genalg, knitr Enhances: MEIGOR, doParallel, foreach License: GPL-2 Archs: x64 MD5sum: af1fd0a0beadc44d943839b4cd2cfab9 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: Logic based ordinary differential equation (ODE) add-on to CellNOptR. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Attila Gabor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_15 git_last_commit: a08ee26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNORode_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORode_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORode_1.38.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Training Signalling Pathway Maps to Biochemical Data with Logic-Based Ordinary Differential Equations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 69 Package: CNTools Version: 1.52.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: x64 MD5sum: dfc54834c5730e00c72becba3e09234f NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: RELEASE_3_15 git_last_commit: 0267b49 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNTools_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNTools_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNTools_1.52.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 54 Package: CNVfilteR Version: 1.10.1 Depends: R (>= 4.1) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 MD5sum: f2f65d9e2dc6c18e11083f6b3d9fa084 NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: RELEASE_3_15 git_last_commit: e2c502a git_last_commit_date: 2022-09-07 Date/Publication: 2022-09-08 source.ver: src/contrib/CNVfilteR_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVfilteR_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVfilteR_1.10.1.tgz vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 156 Package: CNVgears Version: 1.4.0 Depends: R (>= 4.1), data.table Imports: ggplot2 Suggests: VariantAnnotation, DelayedArray, knitr, biomaRt, evobiR, rmarkdown, devtools, cowplot, usethis, scales, testthat, GenomicRanges, cn.mops, R.utils License: GPL-3 MD5sum: 3a7c83353b69fcd05278c29f389d4211 NeedsCompilation: no Title: A Framework of Functions to Combine, Analize and Interpret CNVs Calling Results Description: This package contains a set of functions to perform several type of processing and analysis on CNVs calling pipelines/algorithms results in an integrated manner and regardless of the raw data type (SNPs array or NGS). It provides functions to combine multiple CNV calling results into a single object, filter them, compute CNVRs (CNV Regions) and inheritance patterns, detect genic load, and more. The package is best suited for studies in human family-based cohorts. biocViews: Software, WorkflowStep, Preprocessing Author: Simone Montalbano [cre, aut] Maintainer: Simone Montalbano VignetteBuilder: knitr BugReports: https://github.com/SinomeM/CNVgears/issues git_url: https://git.bioconductor.org/packages/CNVgears git_branch: RELEASE_3_15 git_last_commit: 84c8857 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNVgears_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVgears_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVgears_1.4.0.tgz vignettes: vignettes/CNVgears/inst/doc/CNVgears.html vignetteTitles: CNVgears package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVgears/inst/doc/CNVgears.R dependencyCount: 37 Package: cnvGSA Version: 1.40.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 504b7698665726d20de3a34607465f96 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: RELEASE_3_15 git_last_commit: fb078ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cnvGSA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cnvGSA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cnvGSA_1.40.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 25 Package: CNViz Version: 1.4.0 Depends: R (>= 4.0), shiny (>= 1.5.0) Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR, CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 985aba18f41910a7bfa8f6d79ddf0d4b NeedsCompilation: no Title: Copy Number Visualization Description: CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data. biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq Author: Rebecca Greenblatt [aut, cre] Maintainer: Rebecca Greenblatt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNViz git_branch: RELEASE_3_15 git_last_commit: 837c711 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNViz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNViz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNViz_1.4.0.tgz vignettes: vignettes/CNViz/inst/doc/CNViz.html vignetteTitles: CNViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNViz/inst/doc/CNViz.R dependencyCount: 172 Package: CNVMetrics Version: 1.0.0 Depends: R (>= 4.1) Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods, magrittr, stats, pheatmap, gridExtra, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: cad182300c6ed692c932190c08d51491 NeedsCompilation: no Title: Copy Number Variant Metrics Description: The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics. biocViews: BiologicalQuestion, Software, CopyNumberVariation Author: Astrid Deschênes [aut, cre] (), Pascal Belleau [aut] (), David A. Tuveson [aut], Alexander Krasnitz [aut] Maintainer: Astrid Deschênes URL: https://github.com/krasnitzlab/CNVMetrics, https://krasnitzlab.github.io/CNVMetrics/ VignetteBuilder: knitr BugReports: https://github.com/krasnitzlab/CNVMetrics/issues git_url: https://git.bioconductor.org/packages/CNVMetrics git_branch: RELEASE_3_15 git_last_commit: a8a3020 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNVMetrics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVMetrics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVMetrics_1.0.0.tgz vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html vignetteTitles: Copy number variant metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R dependencyCount: 43 Package: CNVPanelizer Version: 1.28.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: 4b10f9c6af43d3f30095a637109f213f NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: RELEASE_3_15 git_last_commit: f12c17c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNVPanelizer_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVPanelizer_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVPanelizer_1.28.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 113 Package: CNVRanger Version: 1.12.0 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, curatedTCGAData, ensembldb, grid, knitr, regioneR, rmarkdown License: Artistic-2.0 MD5sum: f17391fb4de2636571ae959205dac26c NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva [aut], Marcel Ramos [ctb], Levi Waldron [ctb] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: RELEASE_3_15 git_last_commit: 2823099 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNVRanger_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVRanger_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVRanger_1.12.0.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 56 Package: CNVrd2 Version: 1.34.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 9eb263528e13e1fe502b9e262625d7c2 NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: RELEASE_3_15 git_last_commit: f8660c3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CNVrd2_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVrd2_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVrd2_1.34.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 117 Package: CoCiteStats Version: 1.68.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 60b8d2b5669bb55db17d86556d99a45d NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: RELEASE_3_15 git_last_commit: f72a426 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CoCiteStats_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoCiteStats_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoCiteStats_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 46 Package: COCOA Version: 2.10.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 8ce4150289587ced313011a03e027ccb NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: RELEASE_3_15 git_last_commit: feb68cd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/COCOA_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COCOA_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COCOA_2.10.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 113 Package: codelink Version: 1.64.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 3b2aa87ec3026ead25f5414ae474ccde NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: RELEASE_3_15 git_last_commit: 36f9cc7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/codelink_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/codelink_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/codelink_1.64.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 49 Package: CODEX Version: 1.28.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 37e3733ea4a2c420ced97c70c7bd25e7 NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: RELEASE_3_15 git_last_commit: c707497 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CODEX_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CODEX_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CODEX_1.28.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 47 Package: CoGAPS Version: 3.16.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5 LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: be269a24f0bd573e0d1b646c674c8f60 NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Jeanette Johnson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_15 git_last_commit: 64e95af git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CoGAPS_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoGAPS_3.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoGAPS_3.16.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R importsMe: projectR dependencyCount: 45 Package: cogena Version: 1.30.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 MD5sum: 092c8e596af371e330ba011b803163f7 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: RELEASE_3_15 git_last_commit: ab507f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cogena_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cogena_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cogena_1.30.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 147 Package: cogeqc Version: 1.0.7 Depends: R (>= 4.2.0) Imports: utils, stats, methods, reshape2, ggplot2, ggtree, patchwork, igraph, Biostrings Suggests: testthat (>= 3.0.0), knitr, BiocStyle, rmarkdown, covr License: GPL-3 MD5sum: e065ff2bc1653fef6a45ced772fff17f NeedsCompilation: no Title: Systematic quality checks on comparative genomics analyses Description: cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly quality with BUSCOs; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics. biocViews: Software, GenomeAssembly, ComparativeGenomics, FunctionalGenomics, Phylogenetics, QualityControl, Network Author: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: RELEASE_3_15 git_last_commit: f4e3c31 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-11 source.ver: src/contrib/cogeqc_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/cogeqc_1.0.7.zip mac.binary.ver: bin/macosx/contrib/4.2/cogeqc_1.0.7.tgz vignettes: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html vignetteTitles: Assessing genome assembly, Assessing orthogroup inference, Assessing synteny identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R dependencyCount: 75 Package: Cogito Version: 1.2.0 Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy Imports: BiocManager, rmarkdown, GenomeInfoDb, S4Vectors, AnnotationDbi, graphics, stats, utils, methods, magrittr, ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 3d9e92bcd7efa8dae1e226ba2489a50f NeedsCompilation: no Title: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets Description: Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis. biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing Author: Annika Bürger [cre, aut] Maintainer: Annika Bürger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: RELEASE_3_15 git_last_commit: 111f482 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Cogito_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cogito_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cogito_1.2.0.tgz vignettes: vignettes/Cogito/inst/doc/Cogito.html vignetteTitles: Cogito: Compare annotated genomic intervals tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cogito/inst/doc/Cogito.R dependencyCount: 126 Package: coGPS Version: 1.40.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: e4b8dcf25d62796e12442087cbb03775 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: RELEASE_3_15 git_last_commit: 6be7c22 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/coGPS_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coGPS_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coGPS_1.40.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: COHCAP Version: 1.42.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 Archs: x64 MD5sum: 2f2cf61eb3acb8a186d4eda2209a7c92 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: COHCAP (pronounced "co-cap") provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. Discussion Group: https://sourceforge.net/p/cohcap/discussion/bioconductor/ biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden , Yate-Ching Yuan , Xiwei Wu Maintainer: Charles Warden SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/COHCAP git_branch: RELEASE_3_15 git_last_commit: 93450b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/COHCAP_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COHCAP_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COHCAP_1.42.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R dependencyCount: 14 Package: cola Version: 2.2.0 Depends: R (>= 3.6.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats, GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr, markdown, digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, irlba LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot, flexclust, randomForest, e1071 License: MIT + file LICENSE Archs: x64 MD5sum: c72b1303474c7de4ec5188a0adbaa5c9 NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: RELEASE_3_15 git_last_commit: 93d66ad git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cola_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cola_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cola_2.2.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 64 Package: comapr Version: 1.0.0 Depends: R (>= 4.1.0) Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly, circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel, GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils, Matrix, grid, stats, SummarizedExperiment, plyr, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod License: MIT + file LICENSE MD5sum: ee181a39f4406d5c209788b0b8ae98d5 NeedsCompilation: no Title: Crossover analysis and genetic map construction Description: comapr detects crossover intervals for single gametes from their haplotype states sequences and stores the crossovers in GRanges object. The genetic distances can then be calculated via the mapping functions using estimated crossover rates for maker intervals. Visualisation functions for plotting interval-based genetic map or cumulative genetic distances are implemented, which help reveal the variation of crossovers landscapes across the genome and across individuals. biocViews: Software, SingleCell, Visualization, Genetics Author: Ruqian Lyu [aut, cre] () Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: RELEASE_3_15 git_last_commit: 5a6fa5e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/comapr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/comapr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/comapr_1.0.0.tgz vignettes: vignettes/comapr/inst/doc/getStarted.html, vignettes/comapr/inst/doc/single-sperm-co-analysis.html vignetteTitles: Get-Started-With-comapr, single-sperm-co-analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/comapr/inst/doc/getStarted.R, vignettes/comapr/inst/doc/single-sperm-co-analysis.R dependencyCount: 159 Package: combi Version: 1.8.0 Depends: R (>= 4.0), DBI Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix, BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 5da69b00493f1835c9bfbe764539c80e NeedsCompilation: no Title: Compositional omics model based visual integration Description: This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_15 git_last_commit: 5360215 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/combi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/combi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/combi_1.8.0.tgz vignettes: vignettes/combi/inst/doc/combi.html vignetteTitles: Manual for the combi pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/combi/inst/doc/combi.R dependencyCount: 93 Package: coMET Version: 1.28.0 Depends: R (>= 4.0.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics, showtext License: GPL (>= 2) MD5sum: 3562c275bfa4ac87d7b12d9eaa11726f NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coMET git_branch: RELEASE_3_15 git_last_commit: ea0bb8d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/coMET_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coMET_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coMET_1.28.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R dependencyCount: 151 Package: coMethDMR Version: 1.0.0 Depends: R (>= 4.1) Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub, GenomicRanges, IRanges, lmerTest, methods, stats, utils Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: GPL-3 MD5sum: 1918725f4756783918e5e1b8ed2fe303 NeedsCompilation: no Title: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies Description: coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously. biocViews: DNAMethylation, Epigenetics, MethylationArray, DifferentialMethylation, GenomeWideAssociation Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut] Maintainer: Fernanda Veitzman URL: https://github.com/TransBioInfoLab/coMethDMR VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues git_url: https://git.bioconductor.org/packages/coMethDMR git_branch: RELEASE_3_15 git_last_commit: 1f363f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/coMethDMR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coMethDMR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coMethDMR_1.0.0.tgz vignettes: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel Computing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R dependencyCount: 161 Package: compartmap Version: 1.14.0 Depends: R (>= 4.1.0), SummarizedExperiment, RaggedExperiment, BiocSingular, HDF5Array Imports: GenomicRanges, parallel, grid, ggplot2, reshape2, scales, DelayedArray, rtracklayer, DelayedMatrixStats, Matrix, RMTstat Suggests: covr, testthat, knitr, Rcpp, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: dbaeaecef20dec157fe4791fb4182c6e NeedsCompilation: no Title: Higher-order chromatin domain inference in single cells from scRNA-seq and scATAC-seq Description: Compartmap performs direct inference of higher-order chromatin from scRNA-seq and scATAC-seq. This package implements a James-Stein estimator for computing single-cell level higher-order chromatin domains. Further, we utilize random matrix theory as a method to de-noise correlation matrices to achieve a similar "plaid-like" patterning as observed in Hi-C and scHi-C data. biocViews: Genetics, Epigenetics, ATACSeq, RNASeq, SingleCell Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Hui Shen [aut], Kasper Hansen [aut], Jean-Philippe Fortin [aut] Maintainer: Benjamin Johnson URL: https://github.com/biobenkj/compartmap VignetteBuilder: knitr BugReports: https://github.com/biobenkj/compartmap/issues git_url: https://git.bioconductor.org/packages/compartmap git_branch: RELEASE_3_15 git_last_commit: e33c33b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/compartmap_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/compartmap_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/compartmap_1.14.0.tgz vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html vignetteTitles: Higher-order chromatin inference with compartmap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R dependencyCount: 91 Package: COMPASS Version: 1.34.0 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, progress License: Artistic-2.0 Archs: x64 MD5sum: eeaca25cead75d9fc803a33966ad84c7 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_15 git_last_commit: 26c7da3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/COMPASS_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COMPASS_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COMPASS_1.34.0.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 71 Package: compcodeR Version: 1.32.1 Depends: R (>= 4.0), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, stats, utils, ape, phylolm, matrixStats, grDevices, graphics Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, phytools, phangorn, testthat, ggtree, tidytree, statmod, covr Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 1dc746da6eabbe5a250d87f53c0d4c52 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] (), Paul Bastide [aut] (), Mélina Gallopin [aut] (0000-0002-2431-7825 ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_15 git_last_commit: aba6206 git_last_commit_date: 2022-09-22 Date/Publication: 2022-09-22 source.ver: src/contrib/compcodeR_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/compcodeR_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/compcodeR_1.32.1.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html, vignettes/compcodeR/inst/doc/phylocompcodeR.html vignetteTitles: compcodeR, phylocompcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R, vignettes/compcodeR/inst/doc/phylocompcodeR.R dependencyCount: 83 Package: compEpiTools Version: 1.30.0 Depends: R (>= 3.5.0), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 41a3403d46f0669e02252d19a9265d98 NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: RELEASE_3_15 git_last_commit: 4a6dc81 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/compEpiTools_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/compEpiTools_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/compEpiTools_1.30.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 157 Package: ComplexHeatmap Version: 2.12.1 Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel, codetools Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), gridGraphics, gplots, rmarkdown, Cairo License: MIT + file LICENSE MD5sum: 4bfabad4f6f0d326bfaf5882f0734cce NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_15 git_last_commit: 2c5fe70 git_last_commit_date: 2022-08-08 Date/Publication: 2022-08-09 source.ver: src/contrib/ComplexHeatmap_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ComplexHeatmap_2.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ComplexHeatmap_2.12.1.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap, recoup, countToFPKM importsMe: airpart, ASURAT, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module, CATALYST, celda, CeTF, COCOA, cola, COTAN, cytoKernel, DEComplexDisease, DEGreport, DEP, diffcyt, diffUTR, ELMER, fCCAC, FLAMES, gCrisprTools, GeneTonic, GenomicSuperSignature, gmoviz, GRaNIE, hermes, InterCellar, iSEE, LineagePulse, MatrixQCvis, MesKit, microbiomeMarker, MOMA, monaLisa, muscat, musicatk, MWASTools, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, RLSeq, sechm, segmenter, simplifyEnrichment, singleCellTK, sparrow, SPONGE, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow, armada, bulkAnalyseR, conos, IntLIM, missoNet, MitoHEAR, MKomics, PALMO, pkgndep, rKOMICS, RNAseqQC, RVA, scITD, tidyHeatmap, visxhclust, wilson suggestsMe: artMS, bambu, BindingSiteFinder, BrainSABER, clustifyr, CNVRanger, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute, pareg, plotgardener, projectR, QFeatures, scDblFinder, spiky, TCGAbiolinks, TCGAutils, TimeSeriesExperiment, weitrix, NanoporeRNASeq, CIARA, circlize, eclust, grandR, i2dash, IOHanalyzer, MOSS, multipanelfigure, SCpubr, spiralize, tinyarray dependencyCount: 28 Package: CompoundDb Version: 1.0.2 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics, xml2, IRanges, Spectra (>= 1.5.17), MsCoreUtils, MetaboCoreUtils Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19) License: Artistic-2.0 MD5sum: 366a0824fbb78fb17b412ff0c0c05fb7 NeedsCompilation: no Title: Creating and Using (Chemical) Compound Annotation Databases Description: CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. biocViews: MassSpectrometry, Metabolomics, Annotation Author: Jan Stanstrup [aut] (), Johannes Rainer [aut, cre] (), Josep M. Badia [ctb] (), Roger Gine [aut] (), Andrea Vicini [aut] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/CompoundDb VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues git_url: https://git.bioconductor.org/packages/CompoundDb git_branch: RELEASE_3_15 git_last_commit: 5961080 git_last_commit_date: 2022-09-01 Date/Publication: 2022-09-01 source.ver: src/contrib/CompoundDb_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CompoundDb_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CompoundDb_1.0.2.tgz vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html, vignettes/CompoundDb/inst/doc/create-compounddb.html vignetteTitles: Usage of Annotation Resources with the CompoundDb Package, Creating CompoundDb annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R, vignettes/CompoundDb/inst/doc/create-compounddb.R dependencyCount: 100 Package: ComPrAn Version: 1.4.0 Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr, rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio, scales, shinydashboard, shinyjs, stats, tibble, grid Suggests: testthat (>= 2.1.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: 38d7999e4322009dcdcfa071cf14a438 NeedsCompilation: no Title: Complexome Profiling Analysis package Description: This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. biocViews: MassSpectrometry, Proteomics, Visualization Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] () Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: RELEASE_3_15 git_last_commit: 56e25b2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ComPrAn_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ComPrAn_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ComPrAn_1.4.0.tgz vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html, vignettes/ComPrAn/inst/doc/proteinWorkflow.html, vignettes/ComPrAn/inst/doc/SILACcomplexomics.html vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R, vignettes/ComPrAn/inst/doc/proteinWorkflow.R, vignettes/ComPrAn/inst/doc/SILACcomplexomics.R dependencyCount: 100 Package: conclus Version: 1.3.3 Depends: R (>= 4.2) Imports: org.Hs.eg.db, org.Mm.eg.db, dbscan, fpc, factoextra, Biobase, BiocFileCache, parallel, doParallel, foreach, SummarizedExperiment, biomaRt, AnnotationDbi, methods, dplyr, scran, scater, pheatmap, ggplot2, gridExtra, SingleCellExperiment, stats, utils, scales, grDevices, graphics, Rtsne, GEOquery, clusterProfiler, stringr, tools, rlang Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, matrixStats, dynamicTreeCut, testthat License: GPL-3 MD5sum: 887d73b7d94d533a8ad6a2e23365dfc2 NeedsCompilation: no Title: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A Meaningful CONCLUSion Description: CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets. It takes advantage of a consensus clustering approach that greatly simplify sc-RNA-seq data analysis for the user. Of note, CONCLUS does not cover the preprocessing steps of sequencing files obtained following next-generation sequencing. CONCLUS is organized into the following steps: Generation of multiple t-SNE plots with a range of parameters including different selection of genes extracted from PCA. Use the Density-based spatial clustering of applications with noise (DBSCAN) algorithm for idenfication of clusters in each generated t-SNE plot. All DBSCAN results are combined into a cell similarity matrix. The cell similarity matrix is used to define "CONSENSUS" clusters conserved accross the previously defined clustering solutions. Identify marker genes for each concensus cluster. biocViews: Software, Technology, SingleCell, Sequencing, Clustering, ATACSeq, Classification Author: Ilyess Rachedi [cre], Nicolas Descostes [aut], Polina Pavlovich [aut], Christophe Lancrin [aut] Maintainer: Ilyess Rachedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conclus git_branch: master git_last_commit: b631b31 git_last_commit_date: 2022-03-28 Date/Publication: 2022-03-29 source.ver: src/contrib/conclus_1.3.3.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/conclus_1.3.3.tgz vignettes: vignettes/conclus/inst/doc/conclus_vignette.pdf vignetteTitles: conclus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conclus/inst/doc/conclus_vignette.R dependencyCount: 260 Package: condiments Version: 1.4.0 Depends: R (>= 4.0) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph, distinct Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN License: MIT + file LICENSE MD5sum: 0462da597f5209f38003d10a0998ca7e NeedsCompilation: no Title: Differential Topology, Progression and Differentiation Description: This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format. biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Hector Roux de Bezieux [aut, cre] (), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux URL: https://hectorrdb.github.io/condiments/index.html VignetteBuilder: knitr BugReports: https://github.com/HectorRDB/condiments/issues git_url: https://git.bioconductor.org/packages/condiments git_branch: RELEASE_3_15 git_last_commit: b0cab71 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/condiments_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/condiments_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/condiments_1.4.0.tgz vignettes: vignettes/condiments/inst/doc/condiments.html, vignettes/condiments/inst/doc/controls.html, vignettes/condiments/inst/doc/examples.html vignetteTitles: The condiments workflow, Using condiments, Generating more examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/condiments/inst/doc/condiments.R, vignettes/condiments/inst/doc/controls.R, vignettes/condiments/inst/doc/examples.R dependencyCount: 155 Package: CONFESS Version: 1.24.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: 4218d932e63f77522f9e7506e7f599f1 NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: RELEASE_3_15 git_last_commit: f600c5f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CONFESS_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CONFESS_1.24.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 144 Package: consensus Version: 1.14.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: 9aef440c217ecb1087e5cc43723de0ab NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: RELEASE_3_15 git_last_commit: 7399f31 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/consensus_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensus_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensus_1.14.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.60.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 6e6fe4bb114e13da51d1cc2d7901be80 NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: RELEASE_3_15 git_last_commit: f12f54c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ConsensusClusterPlus_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ConsensusClusterPlus_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ConsensusClusterPlus_1.60.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CancerSubtypes, CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, ccml, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 9 Package: consensusDE Version: 1.14.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 8c03a00fa7d1c0024a2963d9634a8b97 NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: RELEASE_3_15 git_last_commit: a3c3766 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/consensusDE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusDE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusDE_1.14.0.tgz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 149 Package: consensusOV Version: 1.18.0 Depends: R (>= 3.6) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1dfe259671e6d20029170d0b76701cbb NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: RELEASE_3_15 git_last_commit: 8310506 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/consensusOV_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusOV_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusOV_1.18.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R dependencyCount: 143 Package: consensusSeekeR Version: 1.24.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: b2fc83db73b8add9a59ad028bbbdc934 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_15 git_last_commit: bed0114 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/consensusSeekeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusSeekeR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusSeekeR_1.24.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes dependencyCount: 49 Package: CONSTANd Version: 1.4.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: 01331a4416b08c13781956a46df5b9b3 NeedsCompilation: no Title: Data normalization by matrix raking Description: Normalizes a data matrix `data` by raking (using the RAS method by Bacharach, see references) the Nrows by Ncols matrix such that the row means and column means equal 1. The result is a normalized data matrix `K=RAS`, a product of row mulipliers `R` and column multipliers `S` with the original matrix `A`. Missing information needs to be presented as `NA` values and not as zero values, because CONSTANd is able to ignore missing values when calculating the mean. Using CONSTANd normalization allows for the direct comparison of values between samples within the same and even across different CONSTANd-normalized data matrices. biocViews: MassSpectrometry, Cheminformatics, Normalization, Preprocessing, DifferentialExpression, Genetics, Transcriptomics, Proteomics Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk Valkenborg [aut, cre] Maintainer: Dirk Valkenborg URL: qcquan.net/constand VignetteBuilder: knitr BugReports: https://github.com/PDiracDelta/CONSTANd/issues git_url: https://git.bioconductor.org/packages/CONSTANd git_branch: RELEASE_3_15 git_last_commit: 302d23c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CONSTANd_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CONSTANd_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CONSTANd_1.4.0.tgz vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html vignetteTitles: CONSTANd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R dependencyCount: 0 Package: contiBAIT Version: 1.24.0 Depends: R (>= 3.5.0), BH (>= 1.51.0.3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>= 0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 6c598b577403b3ab095d7ccc90f3eee0 NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/contiBAIT git_branch: RELEASE_3_15 git_last_commit: c077022 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/contiBAIT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/contiBAIT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/contiBAIT_1.24.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R dependencyCount: 130 Package: conumee Version: 1.30.0 Depends: R (>= 3.5.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: 2000d703b3e3cfbb94f878d15d9d35ba NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: RELEASE_3_15 git_last_commit: d79a2eb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/conumee_1.30.0.tar.gz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 147 Package: convert Version: 1.72.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: c66659ab13a6474d096571e4737790bc NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: RELEASE_3_15 git_last_commit: 516168a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/convert_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/convert_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/convert_1.72.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 9 Package: copa Version: 1.64.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: x64 MD5sum: f921d977b516fb430e22a965ea6ec4a3 NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: RELEASE_3_15 git_last_commit: f777e54 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/copa_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/copa_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/copa_1.64.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 6 Package: copynumber Version: 1.36.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 14575cedec77fca0ae6b9713db3c5bf6 NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen git_url: https://git.bioconductor.org/packages/copynumber git_branch: RELEASE_3_15 git_last_commit: cf02657 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/copynumber_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/copynumber_1.36.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R importsMe: sequenza suggestsMe: PureCN, sigminer dependencyCount: 16 Package: CopyNumberPlots Version: 1.12.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: a22583b15451350bddaf62a4e65c677a NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: RELEASE_3_15 git_last_commit: b6a4fde git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CopyNumberPlots_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CopyNumberPlots_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CopyNumberPlots_1.12.0.tgz vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR, CNViz dependencyCount: 154 Package: CopywriteR Version: 2.28.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: f47204539217905ffb4e7813ffcde140 NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Oscar Krijgsman URL: https://github.com/PeeperLab/CopywriteR git_url: https://git.bioconductor.org/packages/CopywriteR git_branch: RELEASE_3_15 git_last_commit: 5b2a581 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CopywriteR_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CopywriteR_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CopywriteR_2.28.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R dependencyCount: 55 Package: coRdon Version: 1.14.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: ff14d38dbb1b1a987c107a26e9b16016 NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: RELEASE_3_15 git_last_commit: b4c5d77 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/coRdon_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coRdon_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coRdon_1.14.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 57 Package: CoRegNet Version: 1.34.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 44c054cb2acaf837d5f34eba33f742f4 NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegNet git_branch: RELEASE_3_15 git_last_commit: 6c8cd5b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CoRegNet_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoRegNet_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoRegNet_1.34.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R dependencyCount: 44 Package: CoreGx Version: 2.0.2 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang, bench Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL-3 MD5sum: 32b764bdc9b9dbda3b33955c027b0ef7 NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: RELEASE_3_15 git_last_commit: 4bceca0 git_last_commit_date: 2022-05-20 Date/Publication: 2022-05-22 source.ver: src/contrib/CoreGx_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoreGx_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CoreGx_2.0.2.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/LongTable.html vignetteTitles: CoreGx: Class and Function Abstractions, The LongTable Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/LongTable.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: PDATK dependencyCount: 123 Package: Cormotif Version: 1.42.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 0ab8978f683c4e006240fc42e765a9ca NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: RELEASE_3_15 git_last_commit: 71d0377 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Cormotif_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cormotif_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cormotif_1.42.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 13 Package: corral Version: 1.6.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, reshape2, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, scater, testthat License: GPL-2 MD5sum: 5516058e4188dd05d9b242bfa6fa1cce NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data, as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, GeneExpression, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (), Aedin Culhane [aut] () Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: RELEASE_3_15 git_last_commit: 9798002 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/corral_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/corral_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/corral_1.6.0.tgz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependsOnMe: OSCA.advanced dependencyCount: 77 Package: CORREP Version: 1.62.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 732c7da2e83f3b2599facb905cb8f034 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu git_url: https://git.bioconductor.org/packages/CORREP git_branch: RELEASE_3_15 git_last_commit: 0d3d9ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CORREP_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CORREP_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CORREP_1.62.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R dependencyCount: 9 Package: coseq Version: 1.20.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2, scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: f5962b01ccebdfdab00d3deefe602c85 NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau [cre, aut] (), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: RELEASE_3_15 git_last_commit: 2952e7d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/coseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coseq_1.20.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependencyCount: 111 Package: cosmiq Version: 1.30.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: c512c04cd8da4f92a64fe200b717b7a5 NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: RELEASE_3_15 git_last_commit: b8c2bd7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cosmiq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cosmiq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cosmiq_1.30.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 94 Package: cosmosR Version: 1.4.0 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr, rlang, stringr, utils, visNetwork Suggests: testthat, knitr, rmarkdown, piano, ggplot2 License: GPL-3 MD5sum: a990f17e0069dc53fa4239bb19e2590d NeedsCompilation: no Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut] (), Attila Gabor [cre] (), Katharina Zirngibl [aut] () Maintainer: Attila Gabor URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues git_url: https://git.bioconductor.org/packages/cosmosR git_branch: RELEASE_3_15 git_last_commit: c86024e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cosmosR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cosmosR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cosmosR_1.4.0.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 112 Package: COSNet Version: 1.30.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 13bc0deb8018e9281c6511cc46ff1d41 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: RELEASE_3_15 git_last_commit: e1d5909 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/COSNet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COSNet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COSNet_1.30.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: COTAN Version: 1.0.0 Depends: R (>= 4.1) Imports: dplyr, methods, grDevices, Matrix, ggplot2, ggrepel, stats, parallel, tibble, tidyr, basilisk, reticulate, ComplexHeatmap, circlize, grid, scales, utils, rlang, Rfast Suggests: testthat (>= 3.0.0), spelling, knitr, data.table, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, factoextra, Rtsne, plotly, dendextend, BiocStyle, cowplot License: GPL-3 MD5sum: dc9c8c01e71b4d67ec5a69416d52edfa NeedsCompilation: no Title: COexpression Tables ANalysis Description: Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes. biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell Author: Galfrè Silvia Giulia [aut, cre] (), Morandin Francesco [aut] (), Pietrosanto Marco [aut] (), Cremisi Federico [aut] (), Helmer-Citterich Manuela [aut] () Maintainer: Galfrè Silvia Giulia URL: https://github.com/seriph78/COTAN SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/seriph78/COTAN/issues git_url: https://git.bioconductor.org/packages/COTAN git_branch: RELEASE_3_15 git_last_commit: b6a43f1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/COTAN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COTAN_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COTAN_1.0.0.tgz vignettes: vignettes/COTAN/inst/doc/Guided_tutorial.html vignetteTitles: Guided tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COTAN/inst/doc/Guided_tutorial.R dependencyCount: 80 Package: countsimQC Version: 1.14.0 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods Suggests: knitr, testthat License: GPL (>=2) MD5sum: f7937831698f12629fefb66f9423a7d8 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: RELEASE_3_15 git_last_commit: c136ffc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/countsimQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/countsimQC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/countsimQC_1.14.0.tgz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 126 Package: covEB Version: 1.22.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: db5f18c75547077db7b53fe46f21ac92 NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: RELEASE_3_15 git_last_commit: 7059a3f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/covEB_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/covEB_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/covEB_1.22.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 18 Package: CoverageView Version: 1.34.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 0ccb5b74a138f8c41ff088cc3bc68a2d NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: RELEASE_3_15 git_last_commit: 7ddf4ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CoverageView_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CoverageView_1.34.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 45 Package: covRNA Version: 1.22.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 39b5dc7b261cf78c56c3939b7dd76dd4 NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: RELEASE_3_15 git_last_commit: 23d1e0e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/covRNA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/covRNA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/covRNA_1.22.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 59 Package: cpvSNP Version: 1.28.0 Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 4fb59d54086633f8620617665b16cb80 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: RELEASE_3_15 git_last_commit: df0d6a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cpvSNP_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cpvSNP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cpvSNP_1.28.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 117 Package: cqn Version: 1.42.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 18051a772b45bcdee32e677549cfe2b0 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: RELEASE_3_15 git_last_commit: f18a16d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cqn_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cqn_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cqn_1.42.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: tweeDEseq, GeoTcgaData dependencyCount: 17 Package: CRImage Version: 1.44.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 01397af9905e5b2ac24ca7f79e04c2c8 NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: RELEASE_3_15 git_last_commit: 62e387e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CRImage_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CRImage_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CRImage_1.44.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 42 Package: crisprBase Version: 1.0.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, IRanges, S4Vectors, stringr Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e4cd26042fbcae6d1df46de3e110d6f5 NeedsCompilation: no Title: Base functions and classes for CRISPR gRNA design Description: Provides S4 classes for general nucleases, CRISPR nucleases and base editors. Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/crisprBase VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/crisprBase/issues git_url: https://git.bioconductor.org/packages/crisprBase git_branch: RELEASE_3_15 git_last_commit: 9c1d3b6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/crisprBase_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprBase_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprBase_1.0.0.tgz vignettes: vignettes/crisprBase/inst/doc/crisprBase.html vignetteTitles: Introduction to crisprBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R importsMe: crisprBowtie, crisprBwa dependencyCount: 23 Package: crisprBowtie Version: 1.0.0 Depends: methods Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, GenomicRanges, IRanges, Rbowtie, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: a144c663a694fd33b2316620387db66e NeedsCompilation: no Title: Bowtie-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/crisprBowtie VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/crisprBowtie/issues git_url: https://git.bioconductor.org/packages/crisprBowtie git_branch: RELEASE_3_15 git_last_commit: d2006a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/crisprBowtie_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprBowtie_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprBowtie_1.0.0.tgz vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html vignetteTitles: Introduction to crisprBowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R dependencyCount: 75 Package: crisprBwa Version: 1.0.0 Depends: methods Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, Rbwa, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE OS_type: unix MD5sum: 05d0e036a6f21fd99a775fdaefb00c63 NeedsCompilation: no Title: BWA-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bwa. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Currently not supported on Windows machines. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/crisprBwa VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/crisprBwa/issues git_url: https://git.bioconductor.org/packages/crisprBwa git_branch: RELEASE_3_15 git_last_commit: ba7a773 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/crisprBwa_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/crisprBwa_1.0.0.tgz vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html vignetteTitles: Introduction to crisprBwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R dependencyCount: 75 Package: crisprScore Version: 1.0.0 Depends: R (>= 4.1), crisprScoreData Imports: basilisk (>= 1.3.5), BiocGenerics, Biostrings, IRanges, methods, randomForest, reticulate, stringr, utils, XVector Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 3756f4c81c28734c77b2dbce38e44729 NeedsCompilation: no Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs Description: Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines. biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/crisprScore/issues VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/crisprScore git_url: https://git.bioconductor.org/packages/crisprScore git_branch: RELEASE_3_15 git_last_commit: ab81d7d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/crisprScore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprScore_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprScore_1.0.0.tgz vignettes: vignettes/crisprScore/inst/doc/crisprScore.html vignetteTitles: crisprScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R dependencyCount: 102 Package: CRISPRseek Version: 1.36.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, GenomeInfoDb, GenomicRanges, dplyr, keras, mltools Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice, MASS, tensorflow, testthat License: GPL (>= 2) MD5sum: 75375db3c758d7dcac52d57797bb4cee NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for the CRISPR editing system including Base Editors and the Prime Editor for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. The package also output indels and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Paul Scemama, Benjamin R. Holmes, Hervé Pagès, Kai Hu, Hui Mao, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: RELEASE_3_15 git_last_commit: ecb3359 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CRISPRseek_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CRISPRseek_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CRISPRseek_1.35.3.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R dependsOnMe: crisprseekplus importsMe: GUIDEseq, multicrispr dependencyCount: 96 Package: crisprseekplus Version: 1.22.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocManager, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: c3a0045f19ab3d2809a5438ae75a5ece NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new git_url: https://git.bioconductor.org/packages/crisprseekplus git_branch: RELEASE_3_15 git_last_commit: f8bfdb4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/crisprseekplus_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprseekplus_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprseekplus_1.22.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R dependencyCount: 172 Package: CrispRVariants Version: 1.24.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: c5ba526f670d3cba83f79ba3deaa7d6e NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_15 git_last_commit: 52c8b2e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CrispRVariants_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CrispRVariants_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CrispRVariants_1.24.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 92 Package: crlmm Version: 1.54.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit License: Artistic-2.0 MD5sum: 05637757fe0d3bc8a2abc11dc6044af3 NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: RELEASE_3_15 git_last_commit: 1eefa13 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/crlmm_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crlmm_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crlmm_1.54.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R dependsOnMe: MAGAR importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 63 Package: crossmeta Version: 1.22.1 Depends: R (>= 4.0) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0), data.table (>= 1.10.4), edgeR, fdrtool (>= 1.2.15), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), miniUI (>= 0.1.1), methods, oligo (>= 1.38.0), reader(>= 1.0.6), RCurl (>= 1.95.4.11), RSQLite (>= 2.1.1), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), shinyjs (>= 2.0.0), shinyBS (>= 0.61), shinyWidgets (>= 0.5.3), shinypanel (>= 0.1.0), tibble, XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 7db69dd2bd7045b7857cfca870e0bbd0 NeedsCompilation: no Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering URL: https://github.com/alexvpickering/crossmeta SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm) libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm) openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev (csw), openssl@1.1 (brew) VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/crossmeta/issues git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_15 git_last_commit: fb95000 git_last_commit_date: 2022-05-24 Date/Publication: 2022-05-26 source.ver: src/contrib/crossmeta_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/crossmeta_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/crossmeta_1.22.1.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap dependencyCount: 147 Package: CSAR Version: 1.48.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: 444df1cf63bf0c8714d9d3ba43bac1b8 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: RELEASE_3_15 git_last_commit: 786482e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CSAR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSAR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSAR_1.48.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 16 Package: csaw Version: 1.30.1 Depends: R (>= 3.5.0), GenomicRanges, SummarizedExperiment Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, zlibbioc, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 Archs: x64 MD5sum: 512484e206c275bc06f76f917264e166 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: RELEASE_3_15 git_last_commit: 9e3f0f8 git_last_commit_date: 2022-05-06 Date/Publication: 2022-05-15 source.ver: src/contrib/csaw_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/csaw_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/csaw_1.30.1.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R dependsOnMe: csawBook importsMe: diffHic, epigraHMM, extraChIPs, GRaNIE, icetea, NADfinder, vulcan suggestsMe: chipseqDB dependencyCount: 43 Package: csdR Version: 1.2.0 Depends: R (>= 4.1.0) Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr, igraph, dplyr License: GPL-3 Archs: x64 MD5sum: 72c4f7b3f5e86fde542f647fe49de930 NeedsCompilation: yes Title: Differential gene co-expression Description: This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm. biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression, Network Author: Jakob Peder Pettersen [aut, cre] () Maintainer: Jakob Peder Pettersen URL: https://almaaslab.github.io/csdR, https://github.com/AlmaasLab/csdR VignetteBuilder: knitr BugReports: https://github.com/AlmaasLab/csdR/issues git_url: https://git.bioconductor.org/packages/csdR git_branch: RELEASE_3_15 git_last_commit: 5b79024 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/csdR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/csdR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/csdR_1.2.0.tgz vignettes: vignettes/csdR/inst/doc/csdR.html vignetteTitles: csdR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csdR/inst/doc/csdR.R dependencyCount: 112 Package: CSSP Version: 1.34.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: x64 MD5sum: 6e2e514b70b4962f2172485adcc20809 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo git_url: https://git.bioconductor.org/packages/CSSP git_branch: RELEASE_3_15 git_last_commit: 91c182b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CSSP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSSP_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSSP_1.34.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R dependencyCount: 4 Package: CSSQ Version: 1.8.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: 73542bd3482631692a9868bfec5ba894 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: RELEASE_3_15 git_last_commit: 947dfe5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CSSQ_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSSQ_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSSQ_1.8.0.tgz vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 111 Package: ctc Version: 1.70.0 Depends: amap License: GPL-2 MD5sum: c507a6727a2d0886dc10b3feb39bc3a3 NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_15 git_last_commit: 05dc046 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ctc_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ctc_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ctc_1.70.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTDquerier Version: 2.3.1 Depends: R (>= 4.1) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 8bfc9548cbc34da620f5c4fb7b027895 NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [cre] Maintainer: Xavier Escribà-Montagut VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: master git_last_commit: 755fb4a git_last_commit_date: 2021-11-20 Date/Publication: 2021-11-21 source.ver: src/contrib/CTDquerier_2.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CTDquerier_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CTDquerier_2.4.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 74 Package: ctgGEM Version: 1.7.0 Depends: monocle, SummarizedExperiment, Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, Matrix, methods, utils, sincell, TSCAN Suggests: BiocStyle, biomaRt, HSMMSingleCell, irlba, knitr, rmarkdown, VGAM License: GPL(>=2) MD5sum: 9e9d62d4987dd870ac5bdd14e17115e0 NeedsCompilation: no Title: Generating Tree Hierarchy Visualizations from Gene Expression Data Description: Cell Tree Generator for Gene Expression Matrices (ctgGEM) streamlines the building of cell-state hierarchies from single-cell gene expression data across multiple existing tools for improved comparability and reproducibility. It supports pseudotemporal ordering algorithms and visualization tools from monocle, cellTree, TSCAN, sincell, and destiny, and provides a unified output format for integration with downstream data analysis workflows and Cytoscape. biocViews: GeneExpression, Visualization, Sequencing, SingleCell, Clustering, RNASeq, ImmunoOncology, DifferentialExpression, MultipleComparison, QualityControl, DataImport Author: Mark Block [aut], Carrie Minette [aut], Evgeni Radichev [aut], Etienne Gnimpieba [aut], Mariah Hoffman [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ctgGEM git_branch: master git_last_commit: 1b610e6 git_last_commit_date: 2021-10-26 Date/Publication: 2022-01-31 source.ver: src/contrib/ctgGEM_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ctgGEM_1.7.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ctgGEM_1.7.0.tgz vignettes: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.html vignetteTitles: ctgGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.R dependencyCount: 133 Package: cTRAP Version: 1.14.1 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, htmltools, httr, limma, methods, parallel, pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang, scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes License: MIT + file LICENSE MD5sum: b10cb7f8755f9bd4b9b677dad365125e NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: RELEASE_3_15 git_last_commit: 0cfc240 git_last_commit_date: 2022-07-14 Date/Publication: 2022-07-17 source.ver: src/contrib/cTRAP_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cTRAP_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cTRAP_1.14.1.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: identifying candidate causal perturbations from differential gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 156 Package: ctsGE Version: 1.22.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: ba7d2d5131299f70809fcabfeec58a0c NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: RELEASE_3_15 git_last_commit: f44a936 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ctsGE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ctsGE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ctsGE_1.22.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 72 Package: cummeRbund Version: 2.38.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 8808b17b3c9670fad6213be9f1d0dc2f NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: RELEASE_3_15 git_last_commit: 4e6044b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cummeRbund_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cummeRbund_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cummeRbund_2.38.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependencyCount: 149 Package: customCMPdb Version: 1.6.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 4848f3ac14b35c714046bad38ea61d37 NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics,AnnotationHubSoftware Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: RELEASE_3_15 git_last_commit: 2c81c81 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/customCMPdb_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/customCMPdb_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/customCMPdb_1.6.0.tgz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 110 Package: customProDB Version: 1.36.0 Depends: R (>= 3.5.0), IRanges, AnnotationDbi, biomaRt (>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: c494902d58a6cd9d58ca8cd0fd0bb0e4 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: RELEASE_3_15 git_last_commit: cf3989d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/customProDB_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/customProDB_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/customProDB_1.36.0.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R dependencyCount: 101 Package: cyanoFilter Version: 1.4.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, flowClust, cytometree, ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: ee7a9ea38024359af407346643b56c36 NeedsCompilation: no Title: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity Description: An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community. biocViews: FlowCytometry, Clustering, OneChannel Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut] Maintainer: Oluwafemi Olusoji URL: https://github.com/fomotis/cyanoFilter VignetteBuilder: knitr BugReports: https://github.com/fomotis/cyanoFilter/issues git_url: https://git.bioconductor.org/packages/cyanoFilter git_branch: RELEASE_3_15 git_last_commit: 79606e9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cyanoFilter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cyanoFilter_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cyanoFilter_1.4.0.tgz vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying Phytplanktons and Cyanobacteria Population in Flow Cytometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R dependencyCount: 169 Package: cycle Version: 1.50.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 0d530cf21af9d56bfde4a51aae9f1fa2 NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: RELEASE_3_15 git_last_commit: a6cf07e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cycle_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cycle_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cycle_1.50.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 17 Package: cydar Version: 1.20.0 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 MD5sum: 881710808cc1041173d47513398b8d8b NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: RELEASE_3_15 git_last_commit: e330d5f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cydar_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cydar_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cydar_1.20.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 96 Package: CytoDx Version: 1.16.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 9c139cba9e78773c684bb1216f656f3c NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_15 git_last_commit: 9b5b6f8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CytoDx_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoDx_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CytoDx_1.16.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 49 Package: CyTOFpower Version: 1.2.0 Depends: R (>= 4.1) Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods, rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix, SummarizedExperiment, tibble, tidyr Suggests: testthat (>= 3.0.0), BiocStyle, knitr License: LGPL-3 MD5sum: cddd467f2115a54b852ca7c902286c5d NeedsCompilation: no Title: Power analysis for CyTOF experiments Description: This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed. biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod, Software Author: Anne-Maud Ferreira [cre, aut] (), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CyTOFpower git_branch: RELEASE_3_15 git_last_commit: b9ce7df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CyTOFpower_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CyTOFpower_1.2.0.tgz vignettes: vignettes/CyTOFpower/inst/doc/CyTOFpower.html vignetteTitles: Power analysis for CyTOF experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CyTOFpower/inst/doc/CyTOFpower.R dependencyCount: 287 Package: CytoGLMM Version: 1.4.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, speedglm, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: 3cb957c80c1b71dbb6a61cb88e0d3210 NeedsCompilation: no Title: Conditional Differential Analysis for Flow and Mass Cytometry Experiments Description: The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, ImmunoOncology, Regression, StatisticalMethod, Software Author: Christof Seiler [aut, cre] () Maintainer: Christof Seiler URL: https://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM VignetteBuilder: knitr BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues git_url: https://git.bioconductor.org/packages/CytoGLMM git_branch: RELEASE_3_15 git_last_commit: 0a55f80 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CytoGLMM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoGLMM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CytoGLMM_1.4.0.tgz vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html vignetteTitles: CytoGLMM Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R importsMe: CyTOFpower dependencyCount: 172 Package: cytoKernel Version: 1.2.0 Depends: R (>= 4.1) Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: b1db75f53d7be399b39579c55b03519c NeedsCompilation: yes Title: Differential expression using kernel-based score test Description: cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. biocViews: ImmunoOncology, Proteomics, SingleCell, Software, OneChannel, FlowCytometry, DifferentialExpression, GeneExpression, Clustering Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: RELEASE_3_15 git_last_commit: a08ff21 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cytoKernel_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytoKernel_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytoKernel_1.2.0.tgz vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html vignetteTitles: The CytoK user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R dependencyCount: 76 Package: cytolib Version: 2.8.0 Depends: R (>= 3.4) Imports: RcppParallel, RProtoBufLib LinkingTo: Rcpp, BH(>= 1.75.0.0), RProtoBufLib(>= 2.3.5),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: knitr, rmarkdown License: file LICENSE License_restricts_use: yes Archs: x64 MD5sum: c523b7c40f12ab266d38f87da867b1d0 NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: RELEASE_3_15 git_last_commit: 733543e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cytolib_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytolib_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytolib_2.8.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 9 Package: cytomapper Version: 1.8.0 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) Archs: x64 MD5sum: fa5150ef4767003952428777626e98ad NeedsCompilation: no Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut, cre] (), Nicolas Damond [aut] (), Tobias Hoch [ctb] Maintainer: Nils Eling URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues git_url: https://git.bioconductor.org/packages/cytomapper git_branch: RELEASE_3_15 git_last_commit: d6546b7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cytomapper_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytomapper_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytomapper_1.8.0.tgz vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html, vignettes/cytomapper/inst/doc/cytomapper.html vignetteTitles: "On disk storage of images", "Visualization of imaging cytometry data in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R, vignettes/cytomapper/inst/doc/cytomapper.R importsMe: imcRtools dependencyCount: 113 Package: cytoMEM Version: 1.0.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 45b9e02a809140989ddc9b74396bb3da NeedsCompilation: no Title: Marker Enrichment Modeling (MEM) Description: MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD. biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry, DataRepresentation, DataImport, CellBiology, SingleCell, Clustering Author: Sierra Lima [aut] (), Kirsten Diggins [aut] (), Jonathan Irish [aut, cre] () Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: RELEASE_3_15 git_last_commit: cbbb9d8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cytoMEM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytoMEM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytoMEM_1.0.0.tgz vignettes: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R dependencyCount: 24 Package: CytoML Version: 2.8.1 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc, plyr, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, lattice, stats, corpcor, RUnit, tibble, RcppParallel, xml2 LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1), flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: file LICENSE License_restricts_use: yes MD5sum: 91c36c215b5e83a128702dd8aa03f24f NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_15 git_last_commit: f9127bf git_last_commit_date: 2022-08-21 Date/Publication: 2022-08-23 source.ver: src/contrib/CytoML_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoML_2.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CytoML_2.8.1.tgz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R importsMe: FlowSOM suggestsMe: flowWorkspace, openCyto dependencyCount: 125 Package: CytoTree Version: 1.6.0 Depends: R (>= 4.0), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 870d27595113e50321616283174b459e NeedsCompilation: yes Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference toolkit for flow and mass cytometry data. CytoTree is a valuable tool to build a tree-shaped trajectory using flow and mass cytometry data. The application of CytoTree ranges from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation. It offers complete analyzing workflow for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/CytoTree VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/CytoTree/issues git_url: https://git.bioconductor.org/packages/CytoTree git_branch: RELEASE_3_15 git_last_commit: 796abd0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/CytoTree_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CytoTree_1.6.0.tgz vignettes: vignettes/CytoTree/inst/doc/Tutorial.html vignetteTitles: Quick_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoTree/inst/doc/Tutorial.R dependencyCount: 262 Package: dada2 Version: 1.24.0 Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-2 Archs: x64 MD5sum: 7b36235e06c565d7bd737bf89699dd15 NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: RELEASE_3_15 git_last_commit: 889dd0c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dada2_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dada2_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dada2_1.24.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R importsMe: Rbec suggestsMe: mia dependencyCount: 81 Package: dagLogo Version: 1.34.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) Archs: x64 MD5sum: dfe4953fc704192893fe3a989219cbcd NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: RELEASE_3_15 git_last_commit: 7b7df8c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dagLogo_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dagLogo_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dagLogo_1.34.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 158 Package: daMA Version: 1.68.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: f4528602d7efbf26162d4f4d17ea216a NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: RELEASE_3_15 git_last_commit: 7a950c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/daMA_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/daMA_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/daMA_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 865b1c8ed95cfe063e1146da29d2f6e1 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: RELEASE_3_15 git_last_commit: 5a0777b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DAMEfinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DAMEfinder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DAMEfinder_1.8.0.tgz vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 128 Package: DaMiRseq Version: 2.8.0 Depends: R (>= 3.5.0), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 0657d15b6616d3eac788768f1b6ac764 NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: RELEASE_3_15 git_last_commit: e75b440 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DaMiRseq_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DaMiRseq_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DaMiRseq_2.8.0.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 248 Package: DAPAR Version: 1.28.5 Depends: R (>= 4.2.0) Imports: Biobase, MSnbase, DAPARdata (>= 1.26.3), utils, highcharter, foreach Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph, diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR, factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster, forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr, norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr, limma, preprocessCore, stringr, tidyr, impute, gplots, grDevices, reshape2, graphics, stats, methods, ggplot2, RColorBrewer, Matrix, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 5924caae647f2a16a0abbbbf314c197f NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package). biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [aut, cre], Florence Combes [aut], Thomas Burger [aut], Vasile-Cosmin Lazar [ctb], Enora Fremy [ctb], Helene Borges [ctb] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_15 git_last_commit: d484403 git_last_commit_date: 2022-07-14 Date/Publication: 2022-07-14 source.ver: src/contrib/DAPAR_1.28.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/DAPAR_1.28.5.zip mac.binary.ver: bin/macosx/contrib/4.2/DAPAR_1.28.5.tgz vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar dependencyCount: 106 Package: DART Version: 1.44.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 45831961934ac910c93d6840e3ce53f3 NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: RELEASE_3_15 git_last_commit: 5f4f740 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DART_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DART_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DART_1.44.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 12 Package: dasper Version: 1.6.0 Depends: R (>= 4.0) Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, ggpubr, ggrepel, grid, IRanges, magrittr, megadepth, methods, plyranges, readr, reticulate, rtracklayer, S4Vectors, stringr, SummarizedExperiment, tidyr Suggests: AnnotationFilter, BiocStyle, covr, ensembldb, GenomicState, knitr, lifecycle, markdown, recount, RefManageR, rmarkdown, sessioninfo, testthat, tibble License: Artistic-2.0 MD5sum: 4adc5d8309acab4dafaa660a1cc52087 NeedsCompilation: no Title: Detecting abberant splicing events from RNA-sequencing data Description: The aim of dasper is to detect aberrant splicing events from RNA-seq data. dasper will use as input both junction and coverage data from RNA-seq to calculate the deviation of each splicing event in a patient from a set of user-defined controls. dasper uses an unsupervised outlier detection algorithm to score each splicing event in the patient with an outlier score representing the degree to which that splicing event looks abnormal. biocViews: Software, RNASeq, Transcriptomics, AlternativeSplicing, Coverage, Sequencing Author: David Zhang [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: David Zhang URL: https://github.com/dzhang32/dasper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/dasper git_url: https://git.bioconductor.org/packages/dasper git_branch: RELEASE_3_15 git_last_commit: 06e29e2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dasper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dasper_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dasper_1.6.0.tgz vignettes: vignettes/dasper/inst/doc/dasper.html vignetteTitles: Introduction to dasper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dasper/inst/doc/dasper.R importsMe: ODER dependencyCount: 185 Package: dcanr Version: 1.12.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, COSINE, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 2600ba8971f14227332532b772be30a6 NeedsCompilation: no Title: Differential co-expression/association network analysis Description: Methods and an evaluation framework for the inference of differential co-expression/association networks. biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: RELEASE_3_15 git_last_commit: 1f025f1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dcanr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dcanr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dcanr_1.12.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: SingscoreAMLMutations dependencyCount: 31 Package: dce Version: 1.4.99 Depends: R (>= 4.1) Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, magrittr, glue, Rgraphviz, harmonicmeanp, org.Hs.eg.db, logger, shadowtext Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, cowplot, ggplotify, dagitty, lmtest, sandwich, devtools, curatedTCGAData, TCGAutils, SummarizedExperiment, RcppParallel, docopt, CARNIVAL License: GPL-3 MD5sum: 450294d13364ddc48a3c028f1a5a465f NeedsCompilation: no Title: Pathway Enrichment Based on Differential Causal Effects Description: Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network, KEGG Author: Kim Philipp Jablonski [aut, cre] (), Martin Pirkl [aut] Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/dce VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/dce/issues git_url: https://git.bioconductor.org/packages/dce git_branch: RELEASE_3_15 git_last_commit: 7fa5bd3 git_last_commit_date: 2022-07-16 Date/Publication: 2022-07-17 source.ver: src/contrib/dce_1.4.99.tar.gz win.binary.ver: bin/windows/contrib/4.2/dce_1.4.99.zip mac.binary.ver: bin/macosx/contrib/4.2/dce_1.4.99.tgz vignettes: vignettes/dce/inst/doc/dce.html, vignettes/dce/inst/doc/pathway_databases.html vignetteTitles: Get started, Overview of pathway network databases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dce/inst/doc/dce.R, vignettes/dce/inst/doc/pathway_databases.R dependencyCount: 237 Package: dcGSA Version: 1.24.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: b6387d4c4e8efde5d55fe25efe64de34 NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: RELEASE_3_15 git_last_commit: 36b1d50 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dcGSA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dcGSA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dcGSA_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: ddCt Version: 1.52.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: testthat (>= 3.0.0), RUnit License: LGPL-3 MD5sum: 603fd462faabfa5db097225d4f24c1ab NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: RELEASE_3_15 git_last_commit: 29db550 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ddCt_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ddCt_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ddCt_1.52.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 11 Package: ddPCRclust Version: 1.16.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: e15108fc24564b00958ed5b1b5ec8528 NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: RELEASE_3_15 git_last_commit: 86cd63e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ddPCRclust_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ddPCRclust_1.16.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R dependencyCount: 159 Package: dearseq Version: 1.8.4 Depends: R (>= 3.6.0) Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr, matrixStats, methods, patchwork, parallel, pbapply, reshape2, rlang, stats, statmod, survey, tibble, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: 488ae173b60c5de2e74af76f8eb72b0a NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre], Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: RELEASE_3_15 git_last_commit: c1c013e git_last_commit_date: 2022-07-20 Date/Publication: 2022-07-21 source.ver: src/contrib/dearseq_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/dearseq_1.8.4.zip mac.binary.ver: bin/macosx/contrib/4.2/dearseq_1.8.4.tgz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R importsMe: benchdamic suggestsMe: GeoTcgaData, TcGSA dependencyCount: 58 Package: debCAM Version: 1.14.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 MD5sum: ebef90043bfff1be4868635260f94034 NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: RELEASE_3_15 git_last_commit: 5eabb9a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/debCAM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/debCAM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/debCAM_1.14.0.tgz vignettes: vignettes/debCAM/inst/doc/debcam.html vignetteTitles: debCAM User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/debCAM/inst/doc/debcam.R dependencyCount: 115 Package: debrowser Version: 1.24.1 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, knitr License: GPL-3 + file LICENSE MD5sum: b9209746971f31549408fae3fdc1a16c NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_15 git_last_commit: c03e0a0 git_last_commit_date: 2022-08-01 Date/Publication: 2022-08-02 source.ver: src/contrib/debrowser_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/debrowser_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/debrowser_1.24.1.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 213 Package: DECIPHER Version: 2.24.0 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 MD5sum: c4a735fd8fb2d99d179139a57781b6c8 NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: RELEASE_3_15 git_last_commit: 437e600 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DECIPHER_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DECIPHER_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DECIPHER_2.24.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf, vignettes/DECIPHER/inst/doc/GrowingTrees.pdf, vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences, The Magic of Gene Finding, The Double Life of RNA: Uncovering Non-Coding RNAs, Growing phylogenetic trees with TreeLine, Detecting obscure tandem repeats in sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R, vignettes/DECIPHER/inst/doc/GrowingTrees.R, vignettes/DECIPHER/inst/doc/RepeatRepeat.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: mia, openPrimeR, AssessORFData, copyseparator, ensembleTax suggestsMe: MicrobiotaProcess, microbial, pagoo dependencyCount: 34 Package: deco Version: 1.12.0 Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens, rmarkdown License: GPL (>=3) Archs: x64 MD5sum: 1962d229c159302647c2229deaa8e252 NeedsCompilation: no Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling Description: This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels. biocViews: Software, FeatureExtraction, Clustering, MultipleComparison, DifferentialExpression, Transcriptomics, BiomedicalInformatics, Proteomics, Bayesian, GeneExpression, Transcription, Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray, mRNAMicroarray Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Francisco Jose Campos Laborie URL: https://github.com/fjcamlab/deco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deco git_branch: RELEASE_3_15 git_last_commit: f398a46 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/deco_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deco_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deco_1.12.0.tgz vignettes: vignettes/deco/inst/doc/DECO.html vignetteTitles: deco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deco/inst/doc/DECO.R dependencyCount: 121 Package: DEComplexDisease Version: 1.16.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 MD5sum: 49fcef279e5ad6e3a338df7e2364cfcd NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEComplexDisease git_branch: RELEASE_3_15 git_last_commit: fbcf71d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEComplexDisease_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEComplexDisease_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEComplexDisease_1.16.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R dependencyCount: 106 Package: decompTumor2Sig Version: 2.12.0 Depends: R(>= 4.0), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, GenomeInfoDb, readxl Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 Archs: x64 MD5sum: 8c2236cc1763123d86b639d771269b3b NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: RELEASE_3_15 git_last_commit: 8b41d1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/decompTumor2Sig_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/decompTumor2Sig_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/decompTumor2Sig_2.12.0.tgz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 124 Package: DeconRNASeq Version: 1.38.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: a43b89a2a605dc07220137e39fa4e1ff NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: RELEASE_3_15 git_last_commit: 4d5d7dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DeconRNASeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeconRNASeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeconRNASeq_1.38.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R suggestsMe: ADAPTS dependencyCount: 43 Package: decontam Version: 1.16.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: a868ef43f67551a14486a2f04f2abfb8 NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix G.M. Ernst [ctb] () Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: RELEASE_3_15 git_last_commit: a2baf4e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/decontam_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/decontam_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/decontam_1.16.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R importsMe: mia dependencyCount: 42 Package: deconvR Version: 1.2.0 Depends: R (>= 4.1), data.table (>= 1.14.0) Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>= 2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0), stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1), matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8), nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7), tidyr (>= 1.1.3), assertthat Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>= 1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2), reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools (>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator, RefManageR License: Artistic-2.0 MD5sum: d7d390f8b298934e24f687ad5b3ef7cd NeedsCompilation: no Title: Simulation and Deconvolution of Omic Profiles Description: This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier. biocViews: DNAMethylation, Regression, GeneExpression, RNASeq, SingleCell, StatisticalMethod, Transcriptomics Author: İrem B. Gündüz [aut, cre] (), Veronika Ebenal [aut] (), Altuna Akalin [aut] () Maintainer: İrem B. Gündüz URL: https://github.com/BIMSBbioinfo/deconvR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/deconvR git_url: https://git.bioconductor.org/packages/deconvR git_branch: RELEASE_3_15 git_last_commit: 276cd9f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/deconvR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deconvR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deconvR_1.2.0.tgz vignettes: vignettes/deconvR/inst/doc/deconvRVignette.html vignetteTitles: deconvRVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deconvR/inst/doc/deconvRVignette.R dependencyCount: 132 Package: decoupleR Version: 2.2.2 Depends: R (>= 4.0) Imports: broom, dplyr, magrittr, Matrix, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1.0), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel, patchwork License: GPL-3 + file LICENSE MD5sum: e2e27ece43c45839721fdccd6eaae1c8 NeedsCompilation: no Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (), Jesús Vélez-Santiago [aut] (), Jana Braunger [aut] (), Celina Geiss [aut] (), Daniel Dimitrov [aut] (), Sophia Müller-Dott [aut] (), Petr Taus [aut] (), Aurélien Dugourd [aut] (), Christian H. Holland [aut] (), Ricardo O. Ramirez Flores [aut] (), Julio Saez-Rodriguez [aut] () Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues git_url: https://git.bioconductor.org/packages/decoupleR git_branch: RELEASE_3_15 git_last_commit: a28864c git_last_commit_date: 2022-05-09 Date/Publication: 2022-05-15 source.ver: src/contrib/decoupleR_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/decoupleR_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/decoupleR_2.2.2.tgz vignettes: vignettes/decoupleR/inst/doc/decoupleR.html, vignettes/decoupleR/inst/doc/pw_bk.html, vignettes/decoupleR/inst/doc/pw_sc.html, vignettes/decoupleR/inst/doc/tf_bk.html, vignettes/decoupleR/inst/doc/tf_sc.html vignetteTitles: Introduction, Pathway activity inference in bulk RNA-seq, Pathway activity activity inference from scRNA-seq, Transcription factor activity inference in bulk RNA-seq, Transcription factor activity inference from scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R, vignettes/decoupleR/inst/doc/pw_bk.R, vignettes/decoupleR/inst/doc/pw_sc.R, vignettes/decoupleR/inst/doc/tf_bk.R, vignettes/decoupleR/inst/doc/tf_sc.R importsMe: progeny dependencyCount: 48 Package: DeepBlueR Version: 1.22.0 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) MD5sum: 7c16c3f2e23026b9e3daabecafbbcb6a NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing, ImmunoOncology Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepBlueR git_branch: RELEASE_3_15 git_last_commit: 9c3f354 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DeepBlueR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeepBlueR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeepBlueR_1.22.0.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R dependencyCount: 78 Package: DeepPINCS Version: 1.4.0 Depends: keras, R (>= 4.1) Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: d08e323eb2bcf3d001a59213d9974d1a NeedsCompilation: no Title: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning Description: The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences. biocViews: Software, Network, GraphAndNetwork, NeuralNetwork Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: RELEASE_3_15 git_last_commit: ebcac82 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DeepPINCS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeepPINCS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeepPINCS_1.4.0.tgz vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html vignetteTitles: DeepPINCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R importsMe: GenProSeq, VAExprs dependencyCount: 145 Package: deepSNV Version: 1.42.1 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 MD5sum: 16cc673ea1b75b40b77daf9bc13e708e NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: RELEASE_3_15 git_last_commit: 9800af9 git_last_commit_date: 2022-05-31 Date/Publication: 2022-06-02 source.ver: src/contrib/deepSNV_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/deepSNV_1.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/deepSNV_1.42.1.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R importsMe: mitoClone2 suggestsMe: GenomicFiles dependencyCount: 101 Package: DEFormats Version: 1.24.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 90ea651096db36d372d1e7e0218f0d5d NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: RELEASE_3_15 git_last_commit: 8014385 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEFormats_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEFormats_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEFormats_1.24.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal dependencyCount: 98 Package: DegNorm Version: 1.6.1 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) MD5sum: 8d0b94438016332107a9d1786f185e26 NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Bin Xiong and Ji-Ping Wang Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: RELEASE_3_15 git_last_commit: 7279ff0 git_last_commit_date: 2022-06-25 Date/Publication: 2022-06-26 source.ver: src/contrib/DegNorm_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DegNorm_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DegNorm_1.6.1.tgz vignettes: vignettes/DegNorm/inst/doc/DegNorm.html vignetteTitles: DegNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R dependencyCount: 144 Package: DEGraph Version: 1.48.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: b882da58c027daf09ab8bba532e406c0 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: RELEASE_3_15 git_last_commit: 9d2c152 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEGraph_1.48.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 61 Package: DEGreport Version: 1.32.0 Depends: R (>= 3.6.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, lasso2, magrittr, Nozzle.R1, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 54d137c5ae5beafadff7fcfc0566f55b NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: RELEASE_3_15 git_last_commit: 34bf061 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEGreport_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEGreport_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEGreport_1.32.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs dependencyCount: 134 Package: DEGseq Version: 1.50.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) MD5sum: 1f49f0b750e56d388478d1d0a09a63ac NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_15 git_last_commit: 50d7bec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEGseq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEGseq_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEGseq_1.50.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 43 Package: DelayedArray Version: 0.22.0 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.37.0), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 Archs: x64 MD5sum: d9627acc6dbdfd8fbfc488b34a31b4dd NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès , with contributions from Peter Hickey and Aaron Lun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_15 git_last_commit: 4a5afd1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DelayedArray_0.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedArray_0.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedArray_0.22.0.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, DelayedArray / HDF5Array update, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.R dependsOnMe: DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, rhdf5client, SCArray, singleCellTK, TileDBArray, VCFArray, restfulSEData importsMe: AUCell, batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr, celaref, celda, Cepo, ChromSCape, clusterExperiment, compartmap, CRISPRseek, cytomapper, DelayedTensor, DEScan2, DropletUtils, ELMER, EWCE, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, Macarron, mbkmeans, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData, mumosa, netSmooth, NewWave, NxtIRFcore, orthogene, PCAtools, ResidualMatrix, RTCGAToolbox, ScaledMatrix, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle, signatureSearch, SingleCellExperiment, SingleR, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, weitrix, xcore, zellkonverter, celldex, imcdatasets, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, CNVgears, gwascat, hermes, iSEE, MAST, ProteoDisco, S4Vectors, satuRn, SPOTlight, SQLDataFrame, TrajectoryUtils, digitalDLSorteR dependencyCount: 14 Package: DelayedDataFrame Version: 1.12.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray License: GPL-3 Archs: x64 MD5sum: 484367b33f5996446cdb93662b6a7b76 NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: RELEASE_3_15 git_last_commit: 96e0c04 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DelayedDataFrame_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedDataFrame_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedDataFrame_1.12.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R importsMe: VariantExperiment dependencyCount: 15 Package: DelayedMatrixStats Version: 1.18.2 Depends: MatrixGenerics (>= 1.5.3), DelayedArray (>= 0.17.6) Imports: methods, matrixStats (>= 0.60.0), sparseMatrixStats, Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10) Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem, HDF5Array License: MIT + file LICENSE MD5sum: f77477c555d4cbd119c9734e1d21e2eb NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre], Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: RELEASE_3_15 git_last_commit: 694fc89 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-13 source.ver: src/contrib/DelayedMatrixStats_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedMatrixStats_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedMatrixStats_1.18.2.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: AUCell, batchelor, biscuiteer, bsseq, CAGEr, Cepo, compartmap, dmrseq, DropletUtils, FRASER, glmGamPoi, GSVA, methrix, methylSig, mia, minfi, mumosa, NxtIRFcore, orthogene, PCAtools, SCArray, scater, scMerge, scran, scuttle, singleCellTK, SingleR, sparrow, weitrix, celldex suggestsMe: DelayedArray, EWCE, MatrixGenerics, mbkmeans, scPCA, slingshot, TrajectoryUtils, digitalDLSorteR dependencyCount: 17 Package: DelayedRandomArray Version: 1.4.0 Depends: DelayedArray Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 MD5sum: ff40ba0dd030f377369953b90f1541b7 NeedsCompilation: yes Title: Delayed Arrays of Random Values Description: Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters. biocViews: DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/DelayedRandomArray SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/DelayedRandomArray/issues git_url: https://git.bioconductor.org/packages/DelayedRandomArray git_branch: RELEASE_3_15 git_last_commit: e6034e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DelayedRandomArray_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedRandomArray_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedRandomArray_1.4.0.tgz vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R importsMe: DelayedTensor dependencyCount: 19 Package: DelayedTensor Version: 1.2.0 Depends: R (>= 4.1.0) Imports: methods, utils, DelayedArray, HDF5Array, BiocSingular, rTensor, DelayedRandomArray, irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 Archs: x64 MD5sum: 691432f082733fae2567f208af4d597b NeedsCompilation: no Title: R package for sparse and out-of-core arithmetic and decomposition of Tensor Description: DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum. biocViews: Software, Infrastructure, DataRepresentation, DimensionReduction Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues git_url: https://git.bioconductor.org/packages/DelayedTensor git_branch: RELEASE_3_15 git_last_commit: 1ed37e2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DelayedTensor_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedTensor_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedTensor_1.2.0.tgz vignettes: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.html vignetteTitles: DelayedTensor, TensorArithmetic, TensorDecomposition, Einsum hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.R dependencyCount: 42 Package: deltaCaptureC Version: 1.10.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: c8e9ccc12167d7b158dca455937458a9 NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: RELEASE_3_15 git_last_commit: 86b05b7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/deltaCaptureC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deltaCaptureC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deltaCaptureC_1.10.0.tgz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 94 Package: deltaGseg Version: 1.36.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: b6dc73ad7af9189ce4cbe5231dc175d0 NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: RELEASE_3_15 git_last_commit: 4f17946 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/deltaGseg_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deltaGseg_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deltaGseg_1.36.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 55 Package: DeMAND Version: 1.26.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 238ee5c0ef78c7df7512287dff54058c NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: RELEASE_3_15 git_last_commit: 07f4f03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DeMAND_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeMAND_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeMAND_1.26.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.12.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 MD5sum: dbac3fdef3720be19e2bb47bd0b9b5ec NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Shuai Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_15 git_last_commit: f7bf7d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DeMixT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeMixT_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeMixT_1.12.0.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 79 Package: densvis Version: 1.6.1 Imports: Rcpp, basilisk, assertthat, reticulate LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat License: MIT + file LICENSE Archs: x64 MD5sum: dfe755ac1483c82d07d964c2b2f9b98c NeedsCompilation: yes Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: RELEASE_3_15 git_last_commit: 5c05df3 git_last_commit_date: 2022-06-27 Date/Publication: 2022-06-28 source.ver: src/contrib/densvis_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/densvis_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/densvis_1.6.1.tgz vignettes: vignettes/densvis/inst/doc/densvis.html vignetteTitles: Introduction to densvis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/densvis/inst/doc/densvis.R dependsOnMe: OSCA.advanced dependencyCount: 24 Package: DEP Version: 1.18.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 6233a2208e41d84afa77bb8dcef2fd32 NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: RELEASE_3_15 git_last_commit: 5609377 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEP_1.18.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA, RforProteomics dependencyCount: 155 Package: DepecheR Version: 1.12.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: d54a032ad65692929bc9a4a889e685d4 NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre], Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: RELEASE_3_15 git_last_commit: 4a2b9e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DepecheR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DepecheR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DepecheR_1.12.0.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 84 Package: DepInfeR Version: 1.0.0 Depends: R (>= 4.2.0) Imports: matrixStats, glmnet, stats, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble, ggplot2, missForest, pheatmap, RColorBrewer, ggrepel, ggbeeswarm License: GPL-3 Archs: x64 MD5sum: 2d2ff07071493c4d00106561de61b524 NeedsCompilation: no Title: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Description: DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864. biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics, FunctionalGenomics Author: Junyan Lu [aut, cre] (), Alina Batzilla [aut] Maintainer: Junyan Lu VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues git_url: https://git.bioconductor.org/packages/DepInfeR git_branch: RELEASE_3_15 git_last_commit: e79a3af git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DepInfeR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DepInfeR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DepInfeR_1.0.0.tgz vignettes: vignettes/DepInfeR/inst/doc/vignette.html vignetteTitles: DepInfeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DepInfeR/inst/doc/vignette.R dependencyCount: 26 Package: DEqMS Version: 1.14.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: e04a078593af4dba35c6ad5c2de0ad89 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_15 git_last_commit: a30da35 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEqMS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEqMS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEqMS_1.14.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R dependencyCount: 38 Package: derfinder Version: 1.30.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: 831d7dbc72c0a28136e4c6ebab106f6e NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: RELEASE_3_15 git_last_commit: 7dbc750 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/derfinder_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinder_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinder_1.30.0.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: brainflowprobes, derfinderPlot, ODER, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 151 Package: derfinderHelper Version: 1.30.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: fa3f435a1cc0c466fec50347e0e4d8e3 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: RELEASE_3_15 git_last_commit: f687385 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/derfinderHelper_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinderHelper_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinderHelper_1.30.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 12 Package: derfinderPlot Version: 1.30.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: fc4f92ba5d1bb96e788ffc97ca972526 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: RELEASE_3_15 git_last_commit: 4ebf7a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/derfinderPlot_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinderPlot_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinderPlot_1.30.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: brainflowprobes, recountWorkflow suggestsMe: derfinder, regionReport, GenomicState dependencyCount: 168 Package: DEScan2 Version: 1.16.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 MD5sum: 8ad7dfce9760be85736008d033b2cdad NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: RELEASE_3_15 git_last_commit: 0b0ac15 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEScan2_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEScan2_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEScan2_1.16.0.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 127 Package: DESeq2 Version: 1.36.0 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, stats4, locfit, geneplotter, ggplot2, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) Archs: x64 MD5sum: 8b81b3190d25e8135132d17283bb4500 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_15 git_last_commit: 2800b78 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DESeq2_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DESeq2_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DESeq2_1.36.0.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, DEXSeq, metaseqR2, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, Brundle, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, benchdamic, BRGenomics, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, easier, EBSEA, eegc, ERSSA, GDCRNATools, GeneTonic, Glimma, GRaNIE, hermes, HTSFilter, icetea, ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer, microbiomeMarker, MLSeq, multiSight, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat, pcaExplorer, phantasus, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, scBFA, scGPS, SEtools, singleCellTK, SNPhood, spatialHeatmap, srnadiff, systemPipeTools, TBSignatureProfiler, TEKRABber, TimeSeriesExperiment, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, ExpHunterSuite, recountWorkflow, bulkAnalyseR, cinaR, ExpGenetic, HeritSeq, HTSSIP, intePareto, limorhyde2, MetaLonDA, microbial, RNAseqQC, sRNAGenetic, wilson suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, compcodeR, dearseq, derfinder, diffloop, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, EWCE, fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, miRmine, NxtIRFcore, OPWeight, PCAtools, phyloseq, progeny, recount, RUVSeq, scran, sparrow, subSeq, SummarizedBenchmark, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, bakR, cellpypes, conos, FateID, GeoTcgaData, glmmSeq, grandR, lfc, metaRNASeq, RaceID, seqgendiff, Seurat, volcano3D dependencyCount: 92 Package: DEsingle Version: 1.16.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 Archs: x64 MD5sum: 12cc099a6d39ae067122368ac4551c6e NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_15 git_last_commit: ab2de63 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEsingle_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEsingle_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEsingle_1.16.0.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 38 Package: destiny Version: 3.10.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse, gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq, org.Mm.eg.db, scran, repr Enhances: rgl, SingleCellExperiment License: GPL-3 MD5sum: ae96ce92f4a3f381bc0d740e5012a37f NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (), Laleh Haghverdi [ctb], Maren Büttner [ctb] (), Fabian Theis [ctb] (), Carsten Marr [ctb] (), Florian Büttner [ctb] () Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: RELEASE_3_15 git_last_commit: b2ed76b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/destiny_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/destiny_3.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/destiny_3.10.0.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html, vignettes/destiny/inst/doc/Diffusion-Maps.html, vignettes/destiny/inst/doc/DPT.html, vignettes/destiny/inst/doc/Gene-Relevance.html, vignettes/destiny/inst/doc/Global-Sigma.html, vignettes/destiny/inst/doc/tidyverse.html vignetteTitles: Reproduce the Diffusion Map vignette with the supplied data(), destiny main vignette: Start here!, destiny 2.0 brought the Diffusion Pseudo Time (DPT) class, detecting relevant genes with destiny 3, The effects of a global vs. local kernel, tidyverse and ggplot integration with destiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R, vignettes/destiny/inst/doc/Diffusion-Maps.R, vignettes/destiny/inst/doc/DPT.R, vignettes/destiny/inst/doc/Gene-Relevance.R, vignettes/destiny/inst/doc/Global-Sigma.R, vignettes/destiny/inst/doc/tidyverse.R importsMe: CytoTree, phemd suggestsMe: CelliD, CellTrails, monocle dependencyCount: 130 Package: DEsubs Version: 1.22.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: ab34b662bb1cffb8b34b28b0f853676b NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_15 git_last_commit: 7f97eda git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEsubs_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEsubs_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEsubs_1.22.0.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 129 Package: DEWSeq Version: 1.10.0 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 99298242f2724e4ee0124e0565d69e2b NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: RELEASE_3_15 git_last_commit: 645467d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEWSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEWSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEWSeq_1.10.0.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 97 Package: DExMA Version: 1.4.5 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct, RColorBrewer, grDevices Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 MD5sum: aa418c9b4acb251bbd17f357aca48b06 NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results. biocViews: DifferentialExpression, GeneExpression, StatisticalMethod, QualityControl Author: Juan Antonio Villatoro-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: RELEASE_3_15 git_last_commit: ca72cd4 git_last_commit_date: 2022-10-07 Date/Publication: 2022-10-09 source.ver: src/contrib/DExMA_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/DExMA_1.4.5.zip mac.binary.ver: bin/macosx/contrib/4.2/DExMA_1.4.5.tgz vignettes: vignettes/DExMA/inst/doc/DExMA.pdf vignetteTitles: Differential Expression Meta-Analysis with DExMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DExMA/inst/doc/DExMA.R dependencyCount: 119 Package: DEXSeq Version: 1.42.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: a89f9fc276733be2ac77a79beb585dee NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: RELEASE_3_15 git_last_commit: d91de62 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DEXSeq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEXSeq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEXSeq_1.42.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR, pasilla, rnaseqDTU importsMe: diffUTR, IntEREst suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq dependencyCount: 114 Package: DFP Version: 1.54.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: b23d1f57df60564538000e5ddbd3be6c NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: RELEASE_3_15 git_last_commit: 725d74a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DFP_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DFP_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DFP_1.54.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 6 Package: DIAlignR Version: 2.4.0 Depends: methods, stats, R (>= 4.0) Imports: zoo (>= 1.8-3), data.table, magrittr, dplyr, tidyr, rlang, mzR (>= 2.18), signal, bit64, reticulate, ggplot2, RSQLite, DBI, ape, phangorn, pracma, RMSNumpress, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: knitr, akima, lattice, scales, gridExtra, latticeExtra, rmarkdown, BiocStyle, BiocParallel, testthat (>= 2.1.0) License: GPL-3 MD5sum: 0df565bde3c4921e15de23ca1a138274 NeedsCompilation: yes Title: Dynamic Programming Based Alignment of MS2 Chromatograms Description: To obtain unbiased proteome coverage from a biological sample, mass-spectrometer is operated in Data Independent Acquisition (DIA) mode. Alignment of these DIA runs establishes consistency and less missing values in complete data-matrix. This package implements dynamic programming with affine gap penalty based approach for pair-wise alignment of analytes. A hybrid approach of global alignment (through MS2 features) and local alignment (with MS2 chromatograms) is implemented in this tool. biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment, Software Author: Shubham Gupta [aut, cre] (), Hannes Rost [aut] (), Justin Sing [aut] Maintainer: Shubham Gupta SystemRequirements: C++14 VignetteBuilder: knitr BugReports: https://github.com/shubham1637/DIAlignR/issues git_url: https://git.bioconductor.org/packages/DIAlignR git_branch: RELEASE_3_15 git_last_commit: fb216bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DIAlignR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DIAlignR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DIAlignR_2.4.0.tgz vignettes: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.html vignetteTitles: MS2 chromatograms based alignment of targeted mass-spectrometry runs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.R dependencyCount: 79 Package: DiffBind Version: 3.6.5 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.0), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.15.3), Rcpp Suggests: BiocStyle, testthat, xtable Enhances: rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 Archs: x64 MD5sum: 74096018c0b88d15e3822a57dae94526 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq, DifferentialPeakCalling, DifferentialMethylation, GeneRegulation, HistoneModification, PeakDetection, BiomedicalInformatics, CellBiology, MultipleComparison, Normalization, ReportWriting, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: RELEASE_3_15 git_last_commit: c1ce415 git_last_commit_date: 2022-10-04 Date/Publication: 2022-10-04 source.ver: src/contrib/DiffBind_3.6.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiffBind_3.6.5.zip mac.binary.ver: bin/macosx/contrib/4.2/DiffBind_3.6.5.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan, Brundle dependencyCount: 142 Package: diffcoexp Version: 1.16.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) Archs: x64 MD5sum: 60717a270ec6b02946e0064954906cb0 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: RELEASE_3_15 git_last_commit: b31f568 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffcoexp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffcoexp_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffcoexp_1.16.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R importsMe: ExpHunterSuite, easyDifferentialGeneCoexpression dependencyCount: 122 Package: diffcyt Version: 1.16.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: d540c5c1803f2cf46ec4dcbe129a211b NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_15 git_last_commit: 007433f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffcyt_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/diffcyt_1.16.0.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: censcyt, cytofWorkflow importsMe: CyTOFpower, treekoR suggestsMe: CATALYST dependencyCount: 213 Package: DifferentialRegulation Version: 1.0.7 Depends: R (>= 4.2.0) Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2 LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 7036c6ce887e8c52f1a1bf18b3895de5 NeedsCompilation: yes Title: Differentially regulated genes from scRNA-seq data Description: DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. In particular, when reads are compatible with multiple genes or multiple splicing versions of a gene (unspliced spliced or ambiguous), the method allocates these multi-mapping reads to the gene of origin and their splicing version. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget Author: Simone Tiberi [aut, cre] () Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/DifferentialRegulation SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/DifferentialRegulation/issues git_url: https://git.bioconductor.org/packages/DifferentialRegulation git_branch: RELEASE_3_15 git_last_commit: 013f193 git_last_commit_date: 2022-07-25 Date/Publication: 2022-07-26 source.ver: src/contrib/DifferentialRegulation_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/DifferentialRegulation_1.0.7.zip mac.binary.ver: bin/macosx/contrib/4.2/DifferentialRegulation_1.0.7.tgz vignettes: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html vignetteTitles: DifferentialRegulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R dependencyCount: 83 Package: diffGeneAnalysis Version: 1.78.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL Archs: x64 MD5sum: e857623657a4cb29887437fe99673b2d NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: RELEASE_3_15 git_last_commit: f747205 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffGeneAnalysis_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffGeneAnalysis_1.78.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffGeneAnalysis_1.78.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.28.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: fecbf75e9ba2ecc8c1efd5d141fd0138 NeedsCompilation: yes Title: Differential Analyis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: RELEASE_3_15 git_last_commit: aff6e4c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffHic_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffHic_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffHic_1.28.0.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 56 Package: DiffLogo Version: 2.20.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) Archs: x64 MD5sum: f4f2976bca10353252fd0d33070d9216 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: RELEASE_3_15 git_last_commit: 29665c6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DiffLogo_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiffLogo_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DiffLogo_2.20.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffloop Version: 1.24.0 Depends: R (>= 3.5.0) Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr, data.table, rtracklayer, pbapply, limma Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: df25a4fe701aaf3171a546b2354fc56c NeedsCompilation: no Title: Identifying differential DNA loops from chromatin topology data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau URL: https://github.com/aryeelab/diffloop VignetteBuilder: knitr BugReports: https://github.com/aryeelab/diffloop/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/diffloop git_branch: RELEASE_3_15 git_last_commit: d5d5f0f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffloop_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffloop_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffloop_1.24.0.tgz vignettes: vignettes/diffloop/inst/doc/diffloop.html vignetteTitles: diffloop: Identifying differential DNA loops from chromatin topology data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R dependencyCount: 127 Package: diffuStats Version: 1.16.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 MD5sum: 9ff123fad97b3b3b2b88e3fb6386fe2e NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: RELEASE_3_15 git_last_commit: e95b24d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffuStats_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffuStats_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffuStats_1.16.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 49 Package: diffUTR Version: 1.4.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq, GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap, ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr, matrixStats, IRanges, ensembldb, viridisLite Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 71588c5ec7dbf5f17ae8ef032a858ca8 NeedsCompilation: no Title: diffUTR: Streamlining differential exon and 3' UTR usage Description: The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR git_url: https://git.bioconductor.org/packages/diffUTR git_branch: RELEASE_3_15 git_last_commit: d5d208a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diffUTR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffUTR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffUTR_1.4.0.tgz vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html, vignettes/diffUTR/inst/doc/diffUTR.html vignetteTitles: diffUTR_diffSplice2, 1_diffUTR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R, vignettes/diffUTR/inst/doc/diffUTR.R dependencyCount: 140 Package: diggit Version: 1.28.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: 4a7053de19c46275fecaaedc573785d0 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: RELEASE_3_15 git_last_commit: 8a14032 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/diggit_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diggit_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diggit_1.28.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 34 Package: Dino Version: 1.2.0 Depends: R (>= 4.0.0) Imports: BiocParallel, BiocSingular, SummarizedExperiment, SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats, parallel, scran, grDevices, stats, methods Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools, ggplot2, gridExtra, ggpubr, grid License: GPL-3 MD5sum: ea3cdef9af61e319915a08647928277c NeedsCompilation: no Title: Normalization of Single-Cell mRNA Sequencing Data Description: Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge. biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Regression, CellBasedAssays Author: Jared Brown [aut, cre] (), Christina Kendziorski [ctb] Maintainer: Jared Brown URL: https://github.com/JBrownBiostat/Dino VignetteBuilder: knitr BugReports: https://github.com/JBrownBiostat/Dino/issues git_url: https://git.bioconductor.org/packages/Dino git_branch: RELEASE_3_15 git_last_commit: 4d333bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Dino_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Dino_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Dino_1.2.0.tgz vignettes: vignettes/Dino/inst/doc/Dino.html vignetteTitles: Normalization by distributional resampling of high throughput single-cell RNA-sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Dino/inst/doc/Dino.R dependencyCount: 183 Package: dir.expiry Version: 1.4.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: 0af9ffb0b84e2889009c796d84133715 NeedsCompilation: no Title: Managing Expiration for Cache Directories Description: Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. biocViews: Software, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: RELEASE_3_15 git_last_commit: 9f95040 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dir.expiry_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dir.expiry_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dir.expiry_1.4.0.tgz vignettes: vignettes/dir.expiry/inst/doc/userguide.html vignetteTitles: Managing directory expiration hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dir.expiry/inst/doc/userguide.R importsMe: basilisk, basilisk.utils, rebook dependencyCount: 2 Package: Director Version: 1.22.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: c94efcbc906e656c50bd66b51b92587e NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: RELEASE_3_15 git_last_commit: 7eb287a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Director_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Director_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Director_1.22.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R dependencyCount: 7 Package: DirichletMultinomial Version: 1.38.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 MD5sum: 51541ce2f6d0f7e3f47ab5c6df2da04c NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_15 git_last_commit: b4de83d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DirichletMultinomial_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DirichletMultinomial_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DirichletMultinomial_1.38.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools dependencyCount: 8 Package: discordant Version: 1.20.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 30959101f6a2a216b0cc911c4185ec44 NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016). biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris [aut, cph, ths] Maintainer: McGrath Max URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_15 git_last_commit: 875ef90 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/discordant_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/discordant_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/discordant_1.20.0.tgz vignettes: vignettes/discordant/inst/doc/Using_discordant.html vignetteTitles: The discordant R Package: A Novel Approach to Differential Correlation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Using_discordant.R dependencyCount: 30 Package: DiscoRhythm Version: 1.12.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 Archs: x64 MD5sum: 6eb2a7d7b28468b3b8f9ce4999cc40bf NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: RELEASE_3_15 git_last_commit: 49ed0c7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DiscoRhythm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiscoRhythm_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DiscoRhythm_1.12.0.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 157 Package: distinct Version: 1.8.0 Depends: R (>= 4.0) Imports: Rcpp, Rfast, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo, Rfast Suggests: knitr, rmarkdown, testthat, UpSetR License: GPL (>= 3) MD5sum: 9f07fbcacbed8f5d551f4933f26a72e1 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: RELEASE_3_15 git_last_commit: 4349ff1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/distinct_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/distinct_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/distinct_1.8.0.tgz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R importsMe: condiments, spatialHeatmap dependencyCount: 94 Package: dittoSeq Version: 1.8.1 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran License: MIT + file LICENSE Archs: x64 MD5sum: 5d7f090dbb95e90f1c19c2e55abb5561 NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: RELEASE_3_15 git_last_commit: fc99201 git_last_commit_date: 2022-05-31 Date/Publication: 2022-06-02 source.ver: src/contrib/dittoSeq_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/dittoSeq_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/dittoSeq_1.8.1.tgz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R suggestsMe: escape, tidySingleCellExperiment, magmaR dependencyCount: 64 Package: divergence Version: 1.12.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 959de6e9379bd35d7cd051f0b0beadcb NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: RELEASE_3_15 git_last_commit: d78c72f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/divergence_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/divergence_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/divergence_1.12.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 25 Package: dks Version: 1.42.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: b9b32538322a14f755d2eb7d7725e693 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: RELEASE_3_15 git_last_commit: 0520e19 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dks_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dks_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dks_1.42.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.10.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: d20f460ddfd226c12241decd37687e47 NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: RELEASE_3_15 git_last_commit: 8a845a7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMCFB_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMCFB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMCFB_1.10.0.tgz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 107 Package: DMCHMM Version: 1.18.0 Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 99856938fdef82d54a7d4de44e4ce764 NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_15 git_last_commit: 870c787 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMCHMM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMCHMM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMCHMM_1.18.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden Markov Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 55 Package: DMRcaller Version: 1.28.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10) Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: b113c5a51638795b3ee849ff6e4d95dc NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_15 git_last_commit: 93763d9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMRcaller_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRcaller_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRcaller_1.28.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 30 Package: DMRcate Version: 2.10.0 Depends: R (>= 4.0.0) Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata License: file LICENSE MD5sum: c9d28e4cf18fef14deb28d089dcdfcbc NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: RELEASE_3_15 git_last_commit: 81e8370 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMRcate_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRcate_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRcate_2.10.0.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 223 Package: DMRforPairs Version: 1.32.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: ba3a9fb4a6f827899a138f049785c5f6 NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ git_url: https://git.bioconductor.org/packages/DMRforPairs git_branch: RELEASE_3_15 git_last_commit: 4033198 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMRforPairs_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRforPairs_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRforPairs_1.32.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R dependencyCount: 147 Package: DMRScan Version: 1.18.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 55595dcfeb3ed2933fcab8f7555d90e7 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: RELEASE_3_15 git_last_commit: 701e841 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DMRScan_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRScan_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRScan_1.18.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html vignetteTitles: DMR Scan Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 25 Package: dmrseq Version: 1.16.0 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: ae26b9920e37aa125aa67f26b6f76751 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_15 git_last_commit: b65f67b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dmrseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dmrseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dmrseq_1.16.0.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 165 Package: DNABarcodeCompatibility Version: 1.12.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 146b83065f83a4d2cd6987c8ac5cbc3f NeedsCompilation: no Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] () Maintainer: Céline Trébeau VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_15 git_last_commit: 9cc4f57 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DNABarcodeCompatibility_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNABarcodeCompatibility_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNABarcodeCompatibility_1.12.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 36 Package: DNABarcodes Version: 1.26.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: cd94c5bff3517562916bd3b8c53aa7ee NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_15 git_last_commit: fa2c537 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DNABarcodes_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNABarcodes_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNABarcodes_1.26.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R importsMe: DNABarcodeCompatibility dependencyCount: 11 Package: DNAcopy Version: 1.70.0 License: GPL (>= 2) Archs: x64 MD5sum: f7c20c6a2b43050327a74085b463b88f NeedsCompilation: yes Title: DNA copy number data analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: RELEASE_3_15 git_last_commit: 9595d0a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DNAcopy_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNAcopy_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNAcopy_1.70.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, maftools, MDTS, MEDIPS, MinimumDistance, QDNAseq, Repitools, SCOPE, snapCGH, cghRA, jointseg, PSCBS suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, nullranges, sesame, ACNE, aroma.cn, aroma.core, bcp, calmate dependencyCount: 0 Package: DNAshapeR Version: 1.24.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 Archs: x64 MD5sum: 5564cfe29ee35b0630206a775ce5b2ed NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: RELEASE_3_15 git_last_commit: bb96a1b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DNAshapeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNAshapeR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNAshapeR_1.24.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 57 Package: DominoEffect Version: 1.16.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 6f0c9172c35dd894f11634325ff73d5d NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: RELEASE_3_15 git_last_commit: 0b764f9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DominoEffect_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DominoEffect_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DominoEffect_1.16.0.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 100 Package: doppelgangR Version: 1.24.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) MD5sum: b0b5bada88beae3d78848b5e761edfba NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: RELEASE_3_15 git_last_commit: 4f87528 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/doppelgangR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/doppelgangR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/doppelgangR_1.24.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 77 Package: Doscheda Version: 1.18.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e399093ef9525caddd949e35de816aa3 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: RELEASE_3_15 git_last_commit: f8c34d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Doscheda_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Doscheda_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Doscheda_1.18.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 156 Package: DOSE Version: 3.22.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), methods, qvalue, reshape2, stats, utils Suggests: prettydoc, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: 9aed018be3daf1002af9c2a14082a5e8 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_15 git_last_commit: 6b711a0 git_last_commit_date: 2022-08-28 Date/Publication: 2022-08-30 source.ver: src/contrib/DOSE_3.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DOSE_3.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DOSE_3.22.1.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot, GDCRNATools, MAGeCKFlute, meshes, miRspongeR, MoonlightR, pareg, Pigengene, ReactomePA, RegEnrich, scTensor, signatureSearch, genekitr, immcp suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS, simplifyEnrichment dependencyCount: 91 Package: doseR Version: 1.12.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 65561dd26236ebaaef438a1ea02197f6 NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: RELEASE_3_15 git_last_commit: bd4934e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/doseR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/doseR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/doseR_1.12.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 71 Package: dpeak Version: 1.8.0 Depends: R (>= 4.0.0), methods, stats, utils, graphics, Rcpp Imports: MASS, IRanges, BSgenome, grDevices, parallel LinkingTo: Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL (>= 2) MD5sum: b8f3d5bcf8fe5aa025adf21f31c694a8 NeedsCompilation: yes Title: dPeak (Deconvolution of Peaks in ChIP-seq Analysis) Description: dPeak is a statistical framework for the high resolution identification of protein-DNA interaction sites using PET and SET ChIP-Seq and ChIP-exo data. It provides computationally efficient and user friendly interface to process ChIP-seq and ChIP-exo data, implement exploratory analysis, fit dPeak model, and export list of predicted binding sites for downstream analysis. biocViews: ChIPSeq, Genetics, Sequencing, Software, Transcription Author: Dongjun Chung, Carter Allen Maintainer: Dongjun Chung SystemRequirements: GNU make, meme, fimo BugReports: https://github.com/dongjunchung/dpeak/issues git_url: https://git.bioconductor.org/packages/dpeak git_branch: RELEASE_3_15 git_last_commit: 0ae2693 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dpeak_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dpeak_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dpeak_1.8.0.tgz vignettes: vignettes/dpeak/inst/doc/dpeak-example.pdf vignetteTitles: dPeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dpeak/inst/doc/dpeak-example.R dependencyCount: 48 Package: drawProteins Version: 1.16.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 494737fbc2530856f1e983b773a0c873 NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: RELEASE_3_15 git_last_commit: 495c016 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/drawProteins_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/drawProteins_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/drawProteins_1.16.0.tgz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R dependencyCount: 61 Package: DRIMSeq Version: 1.24.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: f7f0f2eb0fef70b2052f86181202f225 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: RELEASE_3_15 git_last_commit: 07bf0d3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DRIMSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DRIMSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DRIMSeq_1.24.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS, IsoformSwitchAnalyzeR dependencyCount: 65 Package: DriverNet Version: 1.36.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 08c2b9e2bf1b2f14574a1d4eee2cb899 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: RELEASE_3_15 git_last_commit: 6898634 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DriverNet_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DriverNet_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DriverNet_1.36.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.16.0 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, DelayedArray, DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 MD5sum: 6c14f11158fe7d70636431fe3e6c9b43 NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy [ctb], Dongze He [ctb], Rob Patro [ctb] Maintainer: Jonathan Griffiths SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_15 git_last_commit: a08cd3b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DropletUtils_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DropletUtils_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DropletUtils_1.16.0.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: scCB2, singleCellTK, Spaniel, SpatialExperiment suggestsMe: mumosa, Nebulosa, DropletTestFiles, muscData, SoupX dependencyCount: 52 Package: drugTargetInteractions Version: 1.4.1 Depends: methods, R (>= 4.1) Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb, BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT, EnsDb.Hsapiens.v86 License: Artistic-2.0 Archs: x64 MD5sum: d1e69eb6807837608c467aed570f5485 NeedsCompilation: no Title: Drug-Target Interactions Description: Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, Proteomics, Metabolomics Author: Thomas Girke [cre, aut] Maintainer: Thomas Girke URL: https://github.com/girke-lab/drugTargetInteractions VignetteBuilder: knitr BugReports: https://github.com/girke-lab/drugTargetInteractions git_url: https://git.bioconductor.org/packages/drugTargetInteractions git_branch: RELEASE_3_15 git_last_commit: d7aef70 git_last_commit_date: 2022-10-14 Date/Publication: 2022-10-16 source.ver: src/contrib/drugTargetInteractions_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/drugTargetInteractions_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/drugTargetInteractions_1.4.1.tgz vignettes: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html vignetteTitles: Drug-Target Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R dependencyCount: 122 Package: DrugVsDisease Version: 2.38.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: b8d5c64942e05c1e209b9cba2feb24d7 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: RELEASE_3_15 git_last_commit: 7205b0b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DrugVsDisease_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DrugVsDisease_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DrugVsDisease_2.38.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 129 Package: DSS Version: 2.44.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 247d30950c8004bbed5ff26a4cc8b4db NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: RELEASE_3_15 git_last_commit: b9f4410 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DSS_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DSS_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DSS_2.44.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R importsMe: borealis, DMRcate, kissDE, metaseqR2, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 76 Package: dStruct Version: 1.2.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 2) Archs: x64 MD5sum: c62de44748ac68e9ebb3aa0411af5a8f NeedsCompilation: no Title: Identifying differentially reactive regions from RNA structurome profiling data Description: dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al, Genome Biology, 2019 for the underlying method. biocViews: StatisticalMethod, StructuralPrediction, Sequencing, Software Author: Krishna Choudhary [aut, cre] (), Sharon Aviran [aut] () Maintainer: Krishna Choudhary URL: https://github.com/dataMaster-Kris/dStruct VignetteBuilder: knitr BugReports: https://github.com/dataMaster-Kris/dStruct/issues git_url: https://git.bioconductor.org/packages/dStruct git_branch: RELEASE_3_15 git_last_commit: 55cd897 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dStruct_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dStruct_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dStruct_1.2.0.tgz vignettes: vignettes/dStruct/inst/doc/dStruct.html vignetteTitles: Differential RNA structurome analysis using `dStruct` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dStruct/inst/doc/dStruct.R dependencyCount: 49 Package: DTA Version: 2.42.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 632627566ebb546f62f9780127c11676 NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: RELEASE_3_15 git_last_commit: 6a5ce88 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DTA_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DTA_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DTA_2.42.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R dependencyCount: 5 Package: Dune Version: 1.8.0 Depends: R (>= 3.6) Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: b95f8fd7a1a315ec5d36eae6ee2a3604 NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: RELEASE_3_15 git_last_commit: 9bc5007 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Dune_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Dune_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Dune_1.8.0.tgz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 78 Package: dupRadar Version: 1.26.1 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 2895954010817387634e79acb2b550b6 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein URL: https://www.bioconductor.org/packages/dupRadar, https://ssayols.github.io/dupRadar/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: RELEASE_3_15 git_last_commit: 979b852 git_last_commit_date: 2022-06-27 Date/Publication: 2022-06-28 source.ver: src/contrib/dupRadar_1.26.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/dupRadar_1.26.1.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 10 Package: dyebias Version: 1.56.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 4cf9d5273ec5560f5cb7eee6ec2e045b NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: RELEASE_3_15 git_last_commit: 70cb35f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/dyebias_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dyebias_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dyebias_1.56.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 9 Package: DynDoc Version: 1.74.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 223bfed5b5a307503c9842d121db31e4 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: RELEASE_3_15 git_last_commit: 8de68b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/DynDoc_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DynDoc_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DynDoc_1.74.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easier Version: 1.2.2 Depends: R (>= 4.2.0) Imports: progeny, easierData, dorothea (>= 1.6.0), quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr, DESeq2, utils, dplyr, matrixStats, rlang, BiocParallel, reshape2, rstatix, ggrepel, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment License: MIT + file LICENSE MD5sum: 9bfb4d9d062db5a788e5b3cb63fa9bab NeedsCompilation: no Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (), Federico Marini [aut] (), Arsenij Ustjanzew [aut] (), Francesca Finotello [aut] (), Federica Eduati [aut] () Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easier git_branch: RELEASE_3_15 git_last_commit: 4b0b619 git_last_commit_date: 2022-10-13 Date/Publication: 2022-10-16 source.ver: src/contrib/easier_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/easier_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/easier_1.2.2.tgz vignettes: vignettes/easier/inst/doc/easier_user_manual.html vignetteTitles: easier User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easier/inst/doc/easier_user_manual.R dependencyCount: 206 Package: easyreporting Version: 1.8.0 Depends: R (>= 3.5.0) Imports: rmarkdown, methods, tools, shiny, rlang Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: 0a5d91adc6b670f2100e50c94f9bc1b9 NeedsCompilation: no Title: Helps creating report for improving Reproducible Computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: RELEASE_3_15 git_last_commit: ecb4469 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/easyreporting_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/easyreporting_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/easyreporting_1.8.0.tgz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 46 Package: easyRNASeq Version: 2.32.0 Imports: Biobase (>= 2.50.0), BiocFileCache (>= 1.14.0), BiocGenerics (>= 0.36.0), BiocParallel (>= 1.24.1), biomaRt (>= 2.46.0), Biostrings (>= 2.58.0), edgeR (>= 3.32.0), GenomeInfoDb (>= 1.26.0), genomeIntervals (>= 1.46.0), GenomicAlignments (>= 1.26.0), GenomicRanges (>= 1.42.0), SummarizedExperiment (>= 1.20.0), graphics, IRanges (>= 2.24.0), LSD (>= 4.1-0), locfit, methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.6.0), S4Vectors (>= 0.28.0), ShortRead (>= 1.48.0), utils Suggests: BiocStyle (>= 2.18.0), BSgenome (>= 1.58.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.32) License: Artistic-2.0 MD5sum: 81f66677df8860c061c4f21b3eb1c2ae NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_15 git_last_commit: 83a6ed5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/easyRNASeq_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/easyRNASeq_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/easyRNASeq_2.32.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 106 Package: EBarrays Version: 2.60.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) MD5sum: 6b4783aaa8bfaf6410d09cfda5c6dcd8 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: RELEASE_3_15 git_last_commit: b175805 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBarrays_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBarrays_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBarrays_2.60.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 10 Package: EBcoexpress Version: 1.40.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: x64 MD5sum: 694d213807794a4323cda0a51d74c19d NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: RELEASE_3_15 git_last_commit: bc6bb03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBcoexpress_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBcoexpress_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBcoexpress_1.40.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R suggestsMe: dcanr dependencyCount: 14 Package: EBImage Version: 4.38.0 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL Archs: x64 MD5sum: e355b8e5eebe1f103ab9ef3277d550b5 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_15 git_last_commit: 261110e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBImage_4.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBImage_4.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBImage_4.38.0.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: Cardinal, CRImage, cytomapper, flowcatchR, imageHTS, DonaPLLP2013, furrowSeg, GiNA, nucim importsMe: bnbc, flowCHIC, heatmaps, imcRtools, synapsis, yamss, BioImageDbs, bioimagetools, CropDetectR, GoogleImage2Array, LFApp, LOMAR, RockFab, SAFARI, trackter suggestsMe: HilbertVis, DmelSGI, aroma.core, cooltools, ExpImage, graphx, ijtiff, juicr, lidR, metagear, pliman, ProFound, rcaiman dependencyCount: 24 Package: EBSEA Version: 1.24.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: fae0f12ef50844a394eb4becb9fdadfb NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: RELEASE_3_15 git_last_commit: 1069788 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBSEA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSEA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSEA_1.24.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 94 Package: EBSeq Version: 1.36.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: e874a1a78e884037857f1d581ef9eb43 NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_15 git_last_commit: 4ab8be3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSeq_1.36.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR dependencyCount: 47 Package: EBSeqHMM Version: 1.30.0 Depends: EBSeq License: Artistic-2.0 MD5sum: a1adb553941bd3f6bed584dd5a331395 NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeqHMM git_branch: RELEASE_3_15 git_last_commit: d111a93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EBSeqHMM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSeqHMM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSeqHMM_1.30.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R dependencyCount: 48 Package: ecolitk Version: 1.68.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: b95fff413aacbed61101a79c204ba4e3 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: RELEASE_3_15 git_last_commit: a7d75d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ecolitk_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ecolitk_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ecolitk_1.68.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 6 Package: EDASeq Version: 2.30.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2, rmarkdown License: Artistic-2.0 MD5sum: 9a9ac694cec08dc6df2e663fe96e7975 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_15 git_last_commit: 9a5dd9d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EDASeq_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EDASeq_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EDASeq_2.30.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: RUVSeq importsMe: consensusDE, DaMiRseq, metaseqR2, ribosomeProfilingQC suggestsMe: awst, bigPint, DEScan2, easyreporting, HTSFilter, TCGAbiolinks dependencyCount: 111 Package: edge Version: 2.28.1 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE MD5sum: 65de09b860245c5c7c66ef7fa28bafc3 NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_15 git_last_commit: 4b4a5f1 git_last_commit_date: 2022-09-10 Date/Publication: 2022-09-11 source.ver: src/contrib/edge_2.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/edge_2.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/edge_2.28.1.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 116 Package: edgeR Version: 3.38.4 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: methods, graphics, stats, utils, locfit, Rcpp LinkingTo: Rcpp Suggests: jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi, SummarizedExperiment, org.Hs.eg.db License: GPL (>=2) Archs: x64 MD5sum: ced2fd3b2cb262dc82f2e051a6c26094 NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: http://bioinf.wehi.edu.au/edgeR, https://bioconductor.org/packages/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_15 git_last_commit: f5a3bb5 git_last_commit_date: 2022-08-07 Date/Publication: 2022-08-07 source.ver: src/contrib/edgeR_3.38.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/edgeR_3.38.4.zip mac.binary.ver: bin/macosx/contrib/4.2/edgeR_3.38.4.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, IntEREst, methylMnM, miloR, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample, OSCA.workflows, babel, BALLI, BioInsight importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, autonomics, AWFisher, baySeq, beer, benchdamic, BioQC, censcyt, ChromSCape, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, crossmeta, csaw, DaMiRseq, dce, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, diffcyt, diffHic, diffloop, diffUTR, DMRcate, doseR, DRIMSeq, DropletUtils, easyRNASeq, eegc, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs, GDCRNATools, Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR2, microbiomeMarker, MIGSA, MLSeq, moanin, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PhIPData, ppcseq, PROPER, psichomics, RCM, regsplice, Repitools, ROSeq, scCB2, scde, scone, scran, SEtools, SIMD, SingleCellSignalR, singscore, sparrow, spatialHeatmap, splatter, SPsimSeq, srnadiff, sSNAPPY, standR, STATegRa, sva, TBSignatureProfiler, TCseq, TimeSeriesExperiment, tradeSeq, treekoR, tweeDEseq, vidger, xcore, yarn, zinbwave, emtdata, ExpHunterSuite, recountWorkflow, SingscoreAMLMutations, aIc, bulkAnalyseR, CAMML, CIDER, cinaR, HTSCluster, MetaLonDA, microbial, myTAI, QuasiSeq, RVA, scITD, SCRIP, scRNAtools, SPUTNIK, ssizeRNA, TSGS suggestsMe: ABSSeq, bigPint, biobroom, ClassifyR, clonotypeR, cqn, cydar, dcanr, dearseq, DEScan2, dittoSeq, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, missMethyl, multiMiR, recount, regionReport, ribosomeProfilingQC, satuRn, SeqGate, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, GeoTcgaData, glmmSeq, Platypus, seqgendiff, SIBERG, volcano3D dependencyCount: 10 Package: eegc Version: 1.22.0 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 Archs: x64 MD5sum: a47e815153d5c63971a700d1621e1abb NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eegc git_branch: RELEASE_3_15 git_last_commit: 3b483fc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/eegc_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eegc_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eegc_1.22.0.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R dependencyCount: 157 Package: EGAD Version: 1.24.0 Depends: R(>= 3.5) Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat, rmarkdown, markdown License: GPL-2 MD5sum: 1eadbcda6217ee1189d5325ed77e7ad4 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: RELEASE_3_15 git_last_commit: b578eb3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EGAD_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EGAD_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EGAD_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 62 Package: EGSEA Version: 1.24.0 Depends: R (>= 3.5), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: a6f96949b0ee258ab77dca6007a3e2a5 NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_15 git_last_commit: 07e0135 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EGSEA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EGSEA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EGSEA_1.24.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependsOnMe: EGSEA123 suggestsMe: EGSEAdata dependencyCount: 176 Package: eiR Version: 1.36.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown License: Artistic-2.0 MD5sum: 5639c608c72b1e370f215178e5bdaa7c NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: RELEASE_3_15 git_last_commit: 2e56da4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/eiR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eiR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eiR_1.36.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR: Accelerated Similarity Searching of Small Molecules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 65 Package: eisaR Version: 1.8.0 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR, methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, rtracklayer License: GPL-3 Archs: x64 MD5sum: 30f2cf17aedb3d14962282850bd8338d NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: RELEASE_3_15 git_last_commit: 1d61fdb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/eisaR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eisaR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eisaR_1.8.0.tgz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 29 Package: ELMER Version: 2.20.0 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer, DelayedArray Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: d89fd9389246ee55fb2e16da79869270 NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: RELEASE_3_15 git_last_commit: 414ba02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ELMER_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ELMER_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ELMER_2.20.0.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAbiolinksGUI, TCGAWorkflow dependencyCount: 219 Package: EMDomics Version: 2.26.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 1c7d3882484c362b78a76140bd0fa6a6 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: RELEASE_3_15 git_last_commit: 936b0bb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EMDomics_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EMDomics_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EMDomics_2.26.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 49 Package: EmpiricalBrownsMethod Version: 1.24.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f1ddd53b3fb620db3340d51c8dd3b50c NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: RELEASE_3_15 git_last_commit: 07ae147 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EmpiricalBrownsMethod_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EmpiricalBrownsMethod_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EmpiricalBrownsMethod_1.24.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA dependencyCount: 0 Package: EnhancedVolcano Version: 1.14.0 Depends: ggplot2, ggrepel Imports: methods Suggests: ggalt, ggrastr, RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: 864982d0af4b77f116681ac889b72f4f NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles Lewis [aut] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: RELEASE_3_15 git_last_commit: b8b5a78 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EnhancedVolcano_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnhancedVolcano_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EnhancedVolcano_1.14.0.tgz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R dependencyCount: 38 Package: enhancerHomologSearch Version: 1.2.0 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, rtracklayer, Rcpp, S4Vectors, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb, testthat, TFBSTools License: GPL (>= 2) MD5sum: f5980c9fde22a36fc099691d823fbaa7 NeedsCompilation: yes Title: Identification of putative mammalian orthologs to given enhancer Description: Get ENCODE data of enhancer region via H3K4me1 peaks and search homolog regions for given sequences. The candidates of enhancer homolog regions can be filtered by distance to target TSS. The top candidates from human and mouse will be aligned to each other and then exported as multiple alignments with given enhancer. biocViews: Sequencing, GeneRegulation, Alignment Author: Jianhong Ou [aut, cre] (), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou URL: https://jianhong.github.io/enhancerHomologSearch VignetteBuilder: knitr BugReports: https://github.com/jianhong/enhancerHomologSearch/issues git_url: https://git.bioconductor.org/packages/enhancerHomologSearch git_branch: RELEASE_3_15 git_last_commit: 45c23eb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/enhancerHomologSearch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/enhancerHomologSearch_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/enhancerHomologSearch_1.2.0.tgz vignettes: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html vignetteTitles: enhancerHomologSearch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R dependencyCount: 133 Package: EnMCB Version: 1.8.2 Depends: R (>= 4.0) Imports: methods, stats, survivalROC, glmnet, rms, mboost, Matrix, igraph, survivalsvm, ggplot2, BiocFileCache, boot, e1071, survival, utils Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, minfi, limma, rmarkdown License: GPL-2 Archs: x64 MD5sum: 90b2380f60d84e9946698452e6dd5cf6 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: RELEASE_3_15 git_last_commit: 82f5dd1 git_last_commit_date: 2022-06-08 Date/Publication: 2022-06-09 source.ver: src/contrib/EnMCB_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnMCB_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/EnMCB_1.8.2.tgz vignettes: vignettes/EnMCB/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnMCB/inst/doc/vignette.R dependencyCount: 125 Package: ENmix Version: 1.32.0 Depends: R (>= 3.5.0), parallel, doParallel, foreach, SummarizedExperiment, stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 86d5b6de3377dda6ae5aa9f35a182fa0 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tool kits for quanlity control, analysis and visulization of Illumina DNA methylation arrays. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb] Maintainer: Zongli Xu git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_15 git_last_commit: 7abd1c3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ENmix_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ENmix_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ENmix_1.32.0.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 174 Package: EnrichedHeatmap Version: 1.26.0 Depends: R (>= 3.6.0), methods, grid, ComplexHeatmap (>= 2.11.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: 1e26cdcb681a9a117cde016b12b377f4 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_15 git_last_commit: a4b3125 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EnrichedHeatmap_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnrichedHeatmap_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EnrichedHeatmap_1.26.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R importsMe: extraChIPs, profileplyr suggestsMe: ComplexHeatmap, epistack, InteractiveComplexHeatmap dependencyCount: 40 Package: EnrichmentBrowser Version: 2.26.0 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 183e14ce2851998bbde09cf80e63a00e NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_15 git_last_commit: d0f440d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EnrichmentBrowser_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnrichmentBrowser_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EnrichmentBrowser_2.26.0.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR suggestsMe: GenomicSuperSignature dependencyCount: 88 Package: enrichplot Version: 1.16.2 Depends: R (>= 3.5.0) Imports: aplot, DOSE (>= 3.16.0), ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree, yulab.utils (>= 0.0.4) Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggnewscale, ggrepel (>= 0.9.0), ggstar, treeio, scales, tidytree, rlang, ggtreeExtra, tidydr License: Artistic-2.0 MD5sum: fdea16fdc00fa128121830c4a0e3b8c4 NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (), Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: RELEASE_3_15 git_last_commit: eeb2134 git_last_commit_date: 2022-08-28 Date/Publication: 2022-08-30 source.ver: src/contrib/enrichplot_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/enrichplot_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.2/enrichplot_1.16.2.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maEndToEnd importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes, MicrobiomeProfiler, multiSight, ReactomePA, ExpHunterSuite suggestsMe: methylGSA, pareg dependencyCount: 124 Package: enrichTF Version: 1.12.0 Depends: pipeFrame Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils, methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges, BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr, heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices, magrittr Suggests: knitr, testthat, webshot License: GPL-3 MD5sum: 2ad22dbc02796b1834ecdf075a90ca3b NeedsCompilation: no Title: Transcription Factors Enrichment Analysis Description: As transcription factors (TFs) play a crucial role in regulating the transcription process through binding on the genome alone or in a combinatorial manner, TF enrichment analysis is an efficient and important procedure to locate the candidate functional TFs from a set of experimentally defined regulatory regions. While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model. biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork, Transcription Author: Zheng Wei, Zhana Duren, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/enrichTF VignetteBuilder: knitr BugReports: https://github.com/wzthu/enrichTF/issues git_url: https://git.bioconductor.org/packages/enrichTF git_branch: RELEASE_3_15 git_last_commit: 818d8c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/enrichTF_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/enrichTF_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/enrichTF_1.12.0.tgz vignettes: vignettes/enrichTF/inst/doc/enrichTF.html vignetteTitles: An Introduction to enrichTF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R dependencyCount: 221 Package: ensembldb Version: 2.20.2 Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr, rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 2e985a7c5303b5d50aa22b3888d19ad1 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: RELEASE_3_15 git_last_commit: ac1fb83 git_last_commit_date: 2022-06-16 Date/Publication: 2022-06-16 source.ver: src/contrib/ensembldb_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ensembldb_2.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ensembldb_2.20.2.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, diffUTR, epivizrData, ggbio, Gviz, ldblock, metagene, RITAN, scanMiRApp, TVTB, tximeta, GenomicDistributionsData, scRNAseq, crosstalkr, RNAseqQC, utr.annotation suggestsMe: alpine, CNVRanger, dasper, eisaR, epimutacions, EpiTxDb, fishpond, GenomicFeatures, multicrispr, nullranges, satuRn, wiggleplotr dependencyCount: 100 Package: ensemblVEP Version: 1.38.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: ba2fd0466619c89ef012b1c39885fe2a NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 105) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_15 git_last_commit: 8d6b5a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-07-12 source.ver: src/contrib/ensemblVEP_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ensemblVEP_1.38.0.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: MMAPPR2, TVTB dependencyCount: 99 Package: epialleleR Version: 1.4.0 Depends: R (>= 4.1) Imports: stats, methods, utils, GenomicRanges, BiocGenerics, GenomeInfoDb, SummarizedExperiment, VariantAnnotation, stringi, data.table LinkingTo: Rcpp, BH, Rhtslib, zlibbioc Suggests: RUnit, knitr, rmarkdown, ggplot2, ggstance License: Artistic-2.0 MD5sum: 207a5dbd1b6015e2b686e1788415659d NeedsCompilation: yes Title: Fast, Epiallele-Aware Methylation Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls hypermethylated epiallele frequencies at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes extracting methylation patterns, computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs). biocViews: DNAMethylation, Epigenetics, MethylSeq Author: Oleksii Nikolaienko [aut, cre] () Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/epialleleR SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: RELEASE_3_15 git_last_commit: 74145ed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epialleleR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epialleleR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epialleleR_1.4.0.tgz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html vignetteTitles: epialleleR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R dependencyCount: 100 Package: EpiCompare Version: 1.0.0 Depends: R (>= 4.1.0) Imports: GenomicRanges, genomation, IRanges, reshape2, ggplot2, ChIPseeker, BRGenomics, clusterProfiler, plotly, stringr, dplyr, tidyr, UpSetR, rmarkdown, rtracklayer, AnnotationHub, utils, stats, methods, org.Hs.eg.db, S4Vectors, magrittr, plyranges Suggests: testthat (>= 3.0.0), badger, knitr, htmlwidgets, BiocStyle, data.table, BiocParallel, BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene License: GPL-3 Archs: x64 MD5sum: b4b929acc777154ca85b1930f53d7180 NeedsCompilation: no Title: Comparison, Benchmarking & QC of Epigenetic Datasets Description: EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq Author: Sera Choi [aut, cre] (), Brian Schilder [aut] (), Alan Murphy [aut] (), Nathan Skene [aut] () Maintainer: Sera Choi URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues git_url: https://git.bioconductor.org/packages/EpiCompare git_branch: RELEASE_3_15 git_last_commit: fba3043 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/EpiCompare_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiCompare_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiCompare_1.0.0.tgz vignettes: vignettes/EpiCompare/inst/doc/EpiCompare.html vignetteTitles: EpiCompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiCompare/inst/doc/EpiCompare.R dependencyCount: 221 Package: epidecodeR Version: 1.4.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f480931a4e4b94e277ebc8a88fad6bfa NeedsCompilation: no Title: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation Description: epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes. biocViews: DifferentialExpression, GeneRegulation, HistoneModification, FunctionalPrediction, Transcription, GeneExpression, Epitranscriptomics, Epigenetics, FunctionalGenomics, SystemsBiology, Transcriptomics, ChipOnChip Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut] Maintainer: Kandarp Joshi URL: https://github.com/kandarpRJ/epidecodeR, https://epidecoder.shinyapps.io/shinyapp VignetteBuilder: knitr BugReports: https://github.com/kandarpRJ/epidecodeR/issues git_url: https://git.bioconductor.org/packages/epidecodeR git_branch: RELEASE_3_15 git_last_commit: 2aa2cce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epidecodeR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epidecodeR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epidecodeR_1.4.0.tgz vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html vignetteTitles: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R dependencyCount: 132 Package: EpiDISH Version: 2.12.0 Depends: R (>= 4.1) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: 9c8add9f7b283ee6dd55069fff9df0b3 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of whole blood, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: RELEASE_3_15 git_last_commit: fdb7688 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EpiDISH_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiDISH_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiDISH_2.12.0.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: planet dependencyCount: 22 Package: epigenomix Version: 1.36.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: dd2989c88f9d0e0e8d49cc38dc194d4b NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: RELEASE_3_15 git_last_commit: ce6e102 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epigenomix_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epigenomix_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epigenomix_1.36.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 103 Package: epigraHMM Version: 1.4.0 Depends: R (>= 3.5.0) Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib Suggests: testthat, knitr, rmarkdown, BiocStyle, BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData License: MIT + file LICENSE MD5sum: 326cb69f7a10eb2069238dd6e605a8a6 NeedsCompilation: yes Title: Epigenomic R-based analysis with hidden Markov models Description: epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions. biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics Author: Pedro Baldoni [aut, cre] Maintainer: Pedro Baldoni SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epigraHMM git_branch: RELEASE_3_15 git_last_commit: 82b8a69 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epigraHMM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epigraHMM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epigraHMM_1.4.0.tgz vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html vignetteTitles: Consensus and Differential Peak Calling With epigraHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R dependencyCount: 144 Package: epihet Version: 1.12.0 Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2, foreach, Rtsne, igraph Imports: data.table, doParallel, EntropyExplorer, graphics, stats, grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 6fe726668cce6220af4fec154fea5252 NeedsCompilation: no Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data Description: epihet is an R-package that calculates the epigenetic heterogeneity between cancer cells and/or normal cells. The functions establish a pipeline that take in bisulfite sequencing data from multiple samples and use the data to track similarities and differences in epipolymorphism,proportion of discordantly methylated sequencing reads (PDR),and Shannon entropy values at epialleles that are shared between the samples.epihet can be used to perform analysis on the data by creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA plots can also be created by calculating the differential heterogeneity of the samples. And we construct co-epihet network and perform network analysis. biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich [aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut] Maintainer: Xiaowen Chen URL: https://github.com/TheJacksonLaboratory/epihet VignetteBuilder: knitr BugReports: https://github.com/TheJacksonLaboratory/epihet/issues git_url: https://git.bioconductor.org/packages/epihet git_branch: RELEASE_3_15 git_last_commit: b897b37 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epihet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epihet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epihet_1.12.0.tgz vignettes: vignettes/epihet/inst/doc/epihet.pdf vignetteTitles: epihet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epihet/inst/doc/epihet.R dependencyCount: 166 Package: epimutacions Version: 1.0.3 Depends: R (>= 4.2.0), epimutacionsData Imports: minfi, bumphunter, isotree, robustbase, ggplot2, GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment, stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt, BiocParallel Suggests: AnnotationDbi, AnnotationHub, testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, methods, ensembldb, ExperimentHub, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, grid, gridExtra, grDevices, tibble, Gviz, ggrepel, reshape2, purrr, GenomeInfoDb, Homo.sapiens License: MIT + file LICENSE MD5sum: dde492b5032eb2ee2f3f8b257a1780ce NeedsCompilation: yes Title: Robust outlier identification for DNA methylation data Description: The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Leire Abarrategui [aut, cre], Juan R. Gonzalez [aut], Carlos Ruiz-Arenas [aut], Carles Hernandez-Ferrer [aut] Maintainer: Leire Abarrategui URL: https://github.com/isglobal-brge/epimutacions VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/epimutacions/issues git_url: https://git.bioconductor.org/packages/epimutacions git_branch: RELEASE_3_15 git_last_commit: 546eb34 git_last_commit_date: 2022-06-20 Date/Publication: 2022-06-21 source.ver: src/contrib/epimutacions_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/epimutacions_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/epimutacions_1.0.3.tgz vignettes: vignettes/epimutacions/inst/doc/epimutacions.html vignetteTitles: Detection of epimutations with state of the art methods in methylation data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epimutacions/inst/doc/epimutacions.R dependencyCount: 156 Package: epiNEM Version: 1.20.0 Depends: R (>= 4.1) Imports: BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem, latex2exp Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db License: GPL-3 Archs: x64 MD5sum: 5e5d5fd897d68e5752c37d5c5fc473b5 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/epiNEM/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/epiNEM/issues git_url: https://git.bioconductor.org/packages/epiNEM git_branch: RELEASE_3_15 git_last_commit: c9520c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epiNEM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epiNEM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epiNEM_1.20.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: bnem, dce, nempi suggestsMe: mnem dependencyCount: 108 Package: epistack Version: 1.2.0 Depends: R (>= 4.1) Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors, IRanges, viridisLite, graphics, plotrix, grDevices, stats, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: 52b920a6ebf511b0077839a4331e4e27 NeedsCompilation: no Title: Heatmaps of Stack Profiles from Epigenetic Signals Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression Author: SACI Safia [aut], DEVAILLY Guillaume [cre] Maintainer: DEVAILLY Guillaume VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: RELEASE_3_15 git_last_commit: e6b89b2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epistack_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epistack_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epistack_1.2.0.tgz vignettes: vignettes/epistack/inst/doc/using_epistack.html vignetteTitles: Using epistack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epistack/inst/doc/using_epistack.R dependencyCount: 27 Package: EpiTxDb Version: 1.8.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, GenomicFeatures, GenomicRanges, GenomeInfoDb, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: bad3cd55de80083f7a2592294525450e NeedsCompilation: no Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues git_url: https://git.bioconductor.org/packages/EpiTxDb git_branch: RELEASE_3_15 git_last_commit: 4192e54 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EpiTxDb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiTxDb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiTxDb_1.8.0.tgz vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html, vignettes/EpiTxDb/inst/doc/EpiTxDb.html vignetteTitles: EpiTxDb-creation, EpiTxDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R, vignettes/EpiTxDb/inst/doc/EpiTxDb.R dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3 dependencyCount: 115 Package: epivizr Version: 2.26.0 Depends: R (>= 3.5.0), methods Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi, rmarkdown License: Artistic-2.0 MD5sum: e553285c766d54dbae487f8dce9bbc9d NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: RELEASE_3_15 git_last_commit: 2d145e2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epivizr_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizr_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizr_2.26.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone, scTreeViz importsMe: metavizr dependencyCount: 117 Package: epivizrChart Version: 1.18.0 Depends: R (>= 3.5.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 MD5sum: b8aa64fe3d52d99c57476568cb661a60 NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: RELEASE_3_15 git_last_commit: f58d55b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epivizrChart_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrChart_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrChart_1.18.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 112 Package: epivizrData Version: 1.24.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI, matrixStats License: MIT + file LICENSE MD5sum: b6367662e76f9d91853746813daaf304 NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: RELEASE_3_15 git_last_commit: 8a48f19 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epivizrData_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrData_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrData_1.24.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, metavizr, scTreeViz dependencyCount: 109 Package: epivizrServer Version: 1.24.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3df5128d0122ca4e0947b68363c240e3 NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: RELEASE_3_15 git_last_commit: 951fc22 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epivizrServer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrServer_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrServer_1.24.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr, scTreeViz dependencyCount: 13 Package: epivizrStandalone Version: 1.24.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 3a543b320e2e43bc69fe8e9d17e1b22d NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: RELEASE_3_15 git_last_commit: 64f412f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epivizrStandalone_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrStandalone_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrStandalone_1.24.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr suggestsMe: scTreeViz dependencyCount: 119 Package: erccdashboard Version: 1.30.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: 99d53bc14d6b5bf79110cb3873b1830c NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: RELEASE_3_15 git_last_commit: c0e1e1e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/erccdashboard_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/erccdashboard_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/erccdashboard_1.30.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 53 Package: erma Version: 1.12.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: 6cb07aeaf84a80c5de91843bbb7c1e51 NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: RELEASE_3_15 git_last_commit: 0e226dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/erma_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/erma_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/erma_1.12.0.tgz vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R dependencyCount: 136 Package: ERSSA Version: 1.14.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 79a967c79026371947624d6414e4f2e0 NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: RELEASE_3_15 git_last_commit: 37c2950 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ERSSA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ERSSA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ERSSA_1.14.0.tgz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 96 Package: esATAC Version: 1.18.0 Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicAlignments, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot License: GPL-3 | file LICENSE MD5sum: 84c9c59e20299fa7d8cd94d337f00f1f NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_15 git_last_commit: c659773 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/esATAC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/esATAC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/esATAC_1.18.0.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 223 Package: escape Version: 1.6.0 Depends: R (>= 4.1) Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA, SingleCellExperiment, ggridges, msigdbr, stats, BiocParallel, Matrix, UCell, broom, reshape2, patchwork, MatrixGenerics, utils, rlang, stringr, data.table, SummarizedExperiment, methods Suggests: Seurat, SeuratObject, knitr, rmarkdown, markdown, BiocStyle, testthat, dittoSeq (>= 1.1.2) License: GPL-2 Archs: x64 MD5sum: 368473511859f340a3061762134ac063 NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/escape git_branch: RELEASE_3_15 git_last_commit: 98c38e0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/escape_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/escape_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/escape_1.6.0.tgz vignettes: vignettes/escape/inst/doc/vignette.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/escape/inst/doc/vignette.R suggestsMe: Cepo dependencyCount: 121 Package: esetVis Version: 1.22.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: 56b104bfccf7e06a9bf41065d454e60b NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_15 git_last_commit: bed956e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/esetVis_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/esetVis_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/esetVis_1.22.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 56 Package: eudysbiome Version: 1.26.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 7bb41a0ce4bbfff8e41a4d2d8dc60bd5 NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: RELEASE_3_15 git_last_commit: 773206c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/eudysbiome_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eudysbiome_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eudysbiome_1.26.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 35 Package: evaluomeR Version: 1.12.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 9a225f76812d5492bfcda8c0fb04b06c NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: RELEASE_3_15 git_last_commit: 18b5e93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/evaluomeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/evaluomeR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/evaluomeR_1.12.0.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 120 Package: EventPointer Version: 3.4.1 Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings, glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport, fgsea Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: 1248550aa5063c3a06b7796706a3a207 NeedsCompilation: yes Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre], Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo [aut], Ander Aramburu [aut], Angel Rubio [aut] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_15 git_last_commit: d5f1044 git_last_commit_date: 2022-06-09 Date/Publication: 2022-06-09 source.ver: src/contrib/EventPointer_3.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/EventPointer_3.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/EventPointer_3.4.1.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 157 Package: EWCE Version: 1.4.0 Depends: R (>= 4.1), RNOmni (>= 1.0) Imports: stats, utils, methods, ewceData, dplyr, ggplot2, reshape2, limma, stringr, HGNChelper, Matrix, parallel, SingleCellExperiment, SummarizedExperiment, DelayedArray, BiocParallel, orthogene (>= 0.99.8), data.table Suggests: remotes, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2, MAST, DelayedMatrixStats, cowplot, ggdendro, grDevices, grid, gridExtra, scales, magick, badger License: GPL-3 Archs: x64 MD5sum: d0cd59dbe0ac1806cdc8bb00710dc077 NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics, SingleCell Author: Alan Murphy [cre] (), Brian Schilder [aut] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr BugReports: https://github.com/NathanSkene/EWCE/issues git_url: https://git.bioconductor.org/packages/EWCE git_branch: RELEASE_3_15 git_last_commit: 306f434 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/EWCE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EWCE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EWCE_1.4.0.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html, vignettes/EWCE/inst/doc/extended.html vignetteTitles: Getting started, Extended examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R, vignettes/EWCE/inst/doc/extended.R dependencyCount: 207 Package: ExCluster Version: 1.14.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 44da005040c1400dee8fa1ca45ecdb0d NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: RELEASE_3_15 git_last_commit: c5af4b3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExCluster_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ExCluster_1.14.0.tgz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 46 Package: ExiMiR Version: 2.38.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 0b700bb8555a6b91e87e87076a5663f0 NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: RELEASE_3_15 git_last_commit: 0581159 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExiMiR_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExiMiR_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExiMiR_2.38.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 13 Package: exomeCopy Version: 1.42.0 Depends: R (>= 3.5.0), IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) MD5sum: 53e3932389b7b7c3f9fa5566cafe5901 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love git_url: https://git.bioconductor.org/packages/exomeCopy git_branch: RELEASE_3_15 git_last_commit: ba0979c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/exomeCopy_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/exomeCopy_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/exomeCopy_1.42.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT dependencyCount: 30 Package: exomePeak2 Version: 1.8.1 Depends: R (>= 3.5.0), SummarizedExperiment Imports: Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,BSgenome,Biostrings,GenomeInfoDb,BiocParallel,IRanges,S4Vectors,rtracklayer,methods,stats,utils,BiocGenerics,magrittr,speedglm,splines Suggests: knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: c3f244bdd7fc14715716a52fd48f81c6 NeedsCompilation: no Title: Peak Calling and differential analysis for MeRIP-Seq Description: exomePeak2 provides peak detection and differential methylation for Methylated RNA Immunoprecipitation Sequencing (MeRIP-Seq) data. MeRIP-Seq is a commonly applied sequencing assay that measures the location and abundance of RNA modification sites under specific cellular conditions. In practice, the technique is sensitive to PCR amplification biases commonly found in NGS data. In addition, the efficiency of immunoprecipitation often varies between different IP samples. exomePeak2 can perform peak calling and differential analysis independent of GC content bias and IP efficiency changes. biocViews: Sequencing, MethylSeq, RNASeq, Coverage, DifferentialMethylation, DifferentialPeakCalling, PeakDetection Author: Zhen Wei [aut, cre] Maintainer: Zhen Wei VignetteBuilder: knitr BugReports: https://github.com/ZW-xjtlu/exomePeak2/issues git_url: https://git.bioconductor.org/packages/exomePeak2 git_branch: RELEASE_3_15 git_last_commit: 497ed5b git_last_commit_date: 2022-05-18 Date/Publication: 2022-05-19 source.ver: src/contrib/exomePeak2_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/exomePeak2_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/exomePeak2_1.8.1.tgz vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_2.00.html vignetteTitles: The exomePeak2 user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_2.00.R dependencyCount: 122 Package: ExperimentHub Version: 2.4.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.3.6), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: 19c8d8789f27c826dfc2f325c7940da0 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_15 git_last_commit: bdce35d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExperimentHub_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentHub_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentHub_2.4.0.tgz vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, LRcell, SeqSQC, alpineData, BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, crisprScoreData, curatedAdipoChIP, DMRcatedata, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, restfulSEData, RNAmodR.Data, SCATEData, scpdata, sesameData, SimBenchData, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, VectraPolarisData importsMe: BloodGen3Module, coMethDMR, DMRcate, ExperimentHubData, GSEABenchmarkeR, m6Aboost, MACSr, MethReg, methylclock, PhyloProfile, restfulSE, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, easierData, emtdata, FieldEffectCrc, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, imcdatasets, LRcellTypeMarkers, methylclockData, MethylSeqData, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, PhyloProfileData, preciseTADhub, pwrEWAS.data, RLHub, scRNAseq, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, xcoredata suggestsMe: ANF, AnnotationHub, bambu, BioPlex, celaref, CellMapper, ELMER, epimutacions, genomicInstability, HDF5Array, metavizr, missMethyl, MsBackendRawFileReader, muscat, quantiseqr, rawrr, recountmethylation, SingleMoleculeFootprinting, SPOTlight, standR, TCGAbiolinks, xcore, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, tissueTreg dependencyCount: 86 Package: ExperimentHubData Version: 1.22.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 98b3bee1f2934854cecc5e53708ac797 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: RELEASE_3_15 git_last_commit: 8d98ef9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExperimentHubData_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentHubData_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentHubData_1.22.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RNAmodR.Data importsMe: methylclockData suggestsMe: HubPub dependencyCount: 133 Package: ExperimentSubset Version: 1.6.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment, SpatialExperiment, TreeSummarizedExperiment Imports: methods, Matrix, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData, airway License: MIT + file LICENSE MD5sum: d2ddfb120fbcaa5fa148b9bb65ece58f NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (), Muhammad Asif [aut, ths] (), Joshua D. Campbell [aut] () Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: RELEASE_3_15 git_last_commit: 79369da git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExperimentSubset_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentSubset_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentSubset_1.6.0.tgz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 106 Package: ExploreModelMatrix Version: 1.8.0 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE MD5sum: fe3ad2136020541daa34b46e2a50955a NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression Author: Charlotte Soneson [aut, cre] (), Federico Marini [aut] (), Michael Love [aut] (), Florian Geier [aut] (), Michael Stadler [aut] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: RELEASE_3_15 git_last_commit: 03b2798 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExploreModelMatrix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExploreModelMatrix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExploreModelMatrix_1.8.0.tgz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R dependencyCount: 79 Package: ExpressionAtlas Version: 1.24.0 Depends: R (>= 4.1.1), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 2f58c0a1c3edbfa6d8ae230d90dbf877 NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays Maintainer: Pedro Madrigal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_15 git_last_commit: 1fd2126 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ExpressionAtlas_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExpressionAtlas_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExpressionAtlas_1.24.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: spatialHeatmap dependencyCount: 36 Package: extraChIPs Version: 1.0.4 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2, SummarizedExperiment, tibble Imports: BiocIO, broom, ComplexUpset, csaw, dplyr, edgeR, EnrichedHeatmap, forcats, GenomeInfoDb, GenomicInteractions, ggforce, ggrepel, ggside, glue, grDevices, grid, Gviz, InteractionSet, IRanges, limma, methods, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, utils, vctrs, VennDiagram Suggests: BiocStyle, covr, knitr, plyranges, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: f612b3de5e2788ac703e45feae4fea67 NeedsCompilation: no Title: Additional functions for working with ChIP-Seq data Description: This package builds on existing tools and adds some simple but extremely useful capabilities for working with ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented. biocViews: ChIPSeq, HiC, Sequencing, Coverage Author: Stephen Pederson [aut, cre] () Maintainer: Stephen Pederson URL: https://github.com/steveped/extraChIPs VignetteBuilder: knitr BugReports: https://github.com/steveped/extraChIPs/issues git_url: https://git.bioconductor.org/packages/extraChIPs git_branch: RELEASE_3_15 git_last_commit: 303de4a git_last_commit_date: 2022-08-30 Date/Publication: 2022-08-30 source.ver: src/contrib/extraChIPs_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/extraChIPs_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/extraChIPs_1.0.4.tgz vignettes: vignettes/extraChIPs/inst/doc/differential_binding.html, vignettes/extraChIPs/inst/doc/range_based_functions.html vignetteTitles: Differential Binding Analysis, Range-Based Operations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/extraChIPs/inst/doc/differential_binding.R, vignettes/extraChIPs/inst/doc/range_based_functions.R dependencyCount: 177 Package: fabia Version: 2.42.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 012bbf203cdc1b9c65fe35baf76790a0 NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: RELEASE_3_15 git_last_commit: 316294c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fabia_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fabia_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fabia_2.42.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust importsMe: miRSM, mosbi, BcDiag, CSFA suggestsMe: fabiaData dependencyCount: 7 Package: factDesign Version: 1.72.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL Archs: x64 MD5sum: 986ba8f01f4a1c321408236a7ca71fe4 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: RELEASE_3_15 git_last_commit: 78e4c3a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/factDesign_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/factDesign_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/factDesign_1.72.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 6 Package: FamAgg Version: 1.24.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 31e80e58fbf4f9497399747f4a5028b8 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: RELEASE_3_15 git_last_commit: 4d77049 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FamAgg_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FamAgg_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FamAgg_1.24.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 81 Package: famat Version: 1.6.6 Depends: R (>= 4.0) Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 MD5sum: f836f1d3f476d0d18197c77351a32d34 NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Mathieu Charles [aut, cre] () Maintainer: Mathieu Charles URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: RELEASE_3_15 git_last_commit: 91a6ed7 git_last_commit_date: 2022-10-13 Date/Publication: 2022-10-13 source.ver: src/contrib/famat_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/famat_1.6.6.zip mac.binary.ver: bin/macosx/contrib/4.2/famat_1.6.6.tgz vignettes: vignettes/famat/inst/doc/famat.html vignetteTitles: famat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/famat/inst/doc/famat.R dependencyCount: 157 Package: farms Version: 1.48.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: 02f9f6e09b7238ca459c879aaf3cb4f6 NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html git_url: https://git.bioconductor.org/packages/farms git_branch: RELEASE_3_15 git_last_commit: fb571c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/farms_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/farms_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/farms_1.48.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R dependencyCount: 13 Package: fastLiquidAssociation Version: 1.32.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 7d35c21b9925f0a22f804f05035dcc9e NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: RELEASE_3_15 git_last_commit: 5e8cb36 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fastLiquidAssociation_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastLiquidAssociation_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastLiquidAssociation_1.32.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 123 Package: FastqCleaner Version: 1.14.2 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d3dd080eb6cafb132948e6ed5ae16ed6 NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: RELEASE_3_15 git_last_commit: 5fe7f7e git_last_commit_date: 2022-09-25 Date/Publication: 2022-09-27 source.ver: src/contrib/FastqCleaner_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/FastqCleaner_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/FastqCleaner_1.14.2.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.html vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 85 Package: fastreeR Version: 1.0.0 Depends: R (>= 4.2) Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava, stats, stringr, utils Suggests: BiocFileCache, BiocStyle, graphics, knitr, memuse, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 403e15d4890caf379355530734263da9 NeedsCompilation: no Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta Files Description: Calculate distances, build phylogenetic trees or perform hierarchical clustering between the samples of a VCF or FASTA file. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on the VCF or FASTA file for fast execution. biocViews: Phylogenetics, Metagenomics, Clustering Author: Anestis Gkanogiannis [aut, cre] () Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/fastreeR/issues git_url: https://git.bioconductor.org/packages/fastreeR git_branch: RELEASE_3_15 git_last_commit: a27d336 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fastreeR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastreeR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastreeR_1.0.0.tgz vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html vignetteTitles: fastreeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R dependencyCount: 22 Package: fastseg Version: 1.42.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) MD5sum: 17c9ab47261a95798630538b4d91c5ad NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_15 git_last_commit: bac5ad5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fastseg_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastseg_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastseg_1.42.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 17 Package: FCBF Version: 2.4.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE Archs: x64 MD5sum: dd3e72de6917c617324cef6de8300069 NeedsCompilation: no Title: Fast Correlation Based Filter for Feature Selection Description: This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha's implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R . biocViews: GeneTarget, FeatureExtraction, Classification, GeneExpression, SingleCell, ImmunoOncology Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FCBF git_branch: RELEASE_3_15 git_last_commit: 35796df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FCBF_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FCBF_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FCBF_2.4.0.tgz vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R importsMe: fcoex suggestsMe: PubScore dependencyCount: 57 Package: fCCAC Version: 1.22.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 6484083e140927b9858b4a6ac7e0c814 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics Author: Pedro Madrigal [aut, cre] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_15 git_last_commit: 5375f09 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fCCAC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fCCAC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fCCAC_1.22.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 127 Package: fCI Version: 1.26.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: de6f04d78b6f7307fb70ab5bd602f106 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: RELEASE_3_15 git_last_commit: b6f13b3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fCI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fCI_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fCI_1.26.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 39 Package: fcoex Version: 1.10.0 Depends: R (>= 4.1) Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph, grid, intergraph, stringr, clusterProfiler, data.table, grDevices, methods, network, scales, sna, utils, stats, SingleCellExperiment, pathwayPCA, Matrix Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData, scater, schex, gridExtra, scran, Seurat, knitr, rmarkdown License: GPL-3 MD5sum: 8c43cab9f1a8314edb2d250c75362f94 NeedsCompilation: no Title: FCBF-based Co-Expression Networks for Single Cells Description: The fcoex package implements an easy-to use interface to co-expression analysis based on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is adapted from the CEMiTool package, relying on visualizations and code designed and written by CEMiTool's authors. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcoex git_branch: RELEASE_3_15 git_last_commit: 9b34d7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fcoex_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fcoex_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fcoex_1.10.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex_and_seurat.html, vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data integrated with Seurat, fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex_and_seurat.R, vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 147 Package: fcScan Version: 1.10.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: a98200c977a8361652a6a95bd0305fdd NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen [ctb], Pierre Khoueiry [aut, cre] Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_15 git_last_commit: a393868 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fcScan_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fcScan_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fcScan_1.10.0.tgz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 103 Package: fdrame Version: 1.68.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 92b41a1718c1e43cababd627606b1fe8 NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: RELEASE_3_15 git_last_commit: 8dc0627 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fdrame_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fdrame_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fdrame_1.68.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.4.0 Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 Archs: x64 MD5sum: 4e202a8391ae693f7384dca9da5a70e7 NeedsCompilation: yes Title: FEAture SelcTion (FEAST) for Single-cell clustering Description: Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set. biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: RELEASE_3_15 git_last_commit: 41a9fd0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FEAST_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FEAST_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FEAST_1.4.0.tgz vignettes: vignettes/FEAST/inst/doc/FEAST.html vignetteTitles: The FEAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEAST/inst/doc/FEAST.R dependencyCount: 117 Package: fedup Version: 1.4.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE Archs: x64 MD5sum: 087e8f0fe9fff1150f2bc653509c3653 NeedsCompilation: no Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways Description: An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results. biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network Author: Catherine Ross [aut, cre] Maintainer: Catherine Ross URL: https://github.com/rosscm/fedup VignetteBuilder: knitr BugReports: https://github.com/rosscm/fedup/issues git_url: https://git.bioconductor.org/packages/fedup git_branch: RELEASE_3_15 git_last_commit: eb554a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fedup_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fedup_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fedup_1.4.0.tgz vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html, vignettes/fedup/inst/doc/fedup_mutliTest.html, vignettes/fedup/inst/doc/fedup_singleTest.html vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html, fedup_singleTest.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R, vignettes/fedup/inst/doc/fedup_mutliTest.R, vignettes/fedup/inst/doc/fedup_singleTest.R dependencyCount: 79 Package: FELLA Version: 1.16.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 9d7f0a8004bf11269db1a8ba5d860a10 NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_15 git_last_commit: 29297a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FELLA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FELLA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FELLA_1.16.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 37 Package: ffpe Version: 1.40.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: dde666615c5e6a4ceaf84d5f3bbe66a9 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: RELEASE_3_15 git_last_commit: 647a8ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ffpe_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ffpe_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ffpe_1.40.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 167 Package: fgga Version: 1.4.0 Depends: R (>= 4.1), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl Suggests: knitr, rmarkdown, GOstats, PerfMeas, GO.db, BiocGenerics License: GPL-3 MD5sum: 8a15d0fd04c0c4ab43897e40bb3b3077 NeedsCompilation: no Title: Hierarchical ensemble method based on factor graph Description: Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent GO annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models. biocViews: Software, StatisticalMethod, Classification, Network, NetworkInference, SupportVectorMachine, GraphAndNetwork, GO Author: Spetale Flavio [aut, cre], Elizabeth Tapia [aut, ctb] Maintainer: Spetale Flavio URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: RELEASE_3_15 git_last_commit: c867619 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fgga_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fgga_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fgga_1.4.0.tgz vignettes: vignettes/fgga/inst/doc/fgga.html vignetteTitles: FGGA: Factor Graph GO Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgga/inst/doc/fgga.R dependencyCount: 63 Package: FGNet Version: 3.30.0 Depends: R (>= 4.2.0) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager License: GPL (>= 2) MD5sum: c46879ead2ab092255a46a3d20862c14 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: RELEASE_3_15 git_last_commit: 211573f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FGNet_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FGNet_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FGNet_3.30.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 27 Package: fgsea Version: 1.22.0 Depends: R (>= 3.3) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch, Matrix, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery License: MIT + file LICENCE MD5sum: 23b406e1bb2b0afc0b939b7080299ff9 NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: RELEASE_3_15 git_last_commit: e4e203a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fgsea_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fgsea_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fgsea_1.22.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html vignetteTitles: Using fgsea package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R dependsOnMe: gsean, metapone, PPInfer importsMe: ASpediaFI, CelliD, CEMiTool, clustifyr, cTRAP, DOSE, EventPointer, fobitools, lipidr, mCSEA, multiGSEA, NanoTube, omicsViewer, phantasus, piano, RegEnrich, signatureSearch, ViSEAGO, cinaR, DTSEA, scITD suggestsMe: Cepo, decoupleR, gCrisprTools, mdp, pareg, Pi, sparrow, ttgsea, genekitr, grandR, Platypus, rliger dependencyCount: 49 Package: FilterFFPE Version: 1.6.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 Archs: x64 MD5sum: e94b375e79628f738d9e44d73817fc7f NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: RELEASE_3_15 git_last_commit: 20570e8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FilterFFPE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FilterFFPE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FilterFFPE_1.6.0.tgz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 33 Package: FindIT2 Version: 1.2.3 Depends: GenomicRanges, R (>= 3.5.0) Imports: withr, BiocGenerics, GenomeInfoDb, rtracklayer, S4Vectors, GenomicFeatures, dplyr, rlang, patchwork, ggplot2, BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble, tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges, progress, purrr, glmnet, methods Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), TxDb.Athaliana.BioMart.plantsmart28 License: Artistic-2.0 MD5sum: ae8ca145ee986ad779f8f95a674b82ce NeedsCompilation: no Title: find influential TF and Target based on multi-omics data Description: This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation, MultipleComparison, GeneTarget Author: Guandong Shang [aut, cre] () Maintainer: Guandong Shang URL: https://github.com/shangguandong1996/FindIT2 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/FindIT2 git_url: https://git.bioconductor.org/packages/FindIT2 git_branch: RELEASE_3_15 git_last_commit: 17dec46 git_last_commit_date: 2022-05-25 Date/Publication: 2022-05-26 source.ver: src/contrib/FindIT2_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/FindIT2_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.2/FindIT2_1.2.3.tgz vignettes: vignettes/FindIT2/inst/doc/FindIT2.html vignetteTitles: Introduction to FindIT2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R dependencyCount: 125 Package: FISHalyseR Version: 1.30.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: fb036898cba6e4e9f3819f095e6ce0df NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: RELEASE_3_15 git_last_commit: aff180c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FISHalyseR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FISHalyseR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FISHalyseR_1.30.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 25 Package: fishpond Version: 2.2.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, IRanges, SummarizedExperiment, GenomicRanges, matrixStats, svMisc, Rcpp, Matrix, SingleCellExperiment, jsonlite LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma, ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi, pheatmap, Gviz, GenomeInfoDb, data.table License: GPL-2 MD5sum: e82d9e96930c5fb637f34d81993f9ce3 NeedsCompilation: yes Title: Fishpond: downstream methods and tools for expression data Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb], Jeroen Gilis [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/fishpond SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_15 git_last_commit: 5a790b0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fishpond_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fishpond_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fishpond_2.2.0.tgz vignettes: vignettes/fishpond/inst/doc/allelic.html, vignettes/fishpond/inst/doc/swish.html vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and Swish, 1. Swish: DE analysis accounting for inferential uncertainty hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/allelic.R, vignettes/fishpond/inst/doc/swish.R importsMe: singleCellTK suggestsMe: tximeta, tximport dependencyCount: 64 Package: FitHiC Version: 1.22.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 50223050119d1f0730dbf5d4624ec138 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: RELEASE_3_15 git_last_commit: a07bfc1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FitHiC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FitHiC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FitHiC_1.22.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.52.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: x64 MD5sum: 0028939a70d2cfd5af0368e1349eff5b NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography-massspectrometry metabolomics data. biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_15 git_last_commit: 69d19bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flagme_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flagme_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flagme_1.52.0.tgz vignettes: vignettes/flagme/inst/doc/flagme-knitr.pdf, vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data, \texttt{flagme}: Fragment-level analysis of \\ GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme-knitr.R, vignettes/flagme/inst/doc/flagme.R dependencyCount: 133 Package: FLAMES Version: 1.2.2 Imports: basilisk, reticulate, SingleCellExperiment, SummarizedExperiment, Rsamtools, utils, zlibbioc, scater, dplyr, tidyr, magrittr, S4Vectors, scuttle, stats, rtracklayer, igraph, ggbio, GenomicRanges, Matrix, BiocGenerics, ggplot2, scran, ComplexHeatmap, RColorBrewer, circlize, grid, gridExtra, cowplot, stringr, bambu, GenomeInfoDb, withr, Biostrings, GenomicFeatures LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, knitr, rmarkdown, markdown, BiocFileCache License: GPL (>= 2) Archs: x64 MD5sum: 869990b4b11b03287c21b800aa50e2f4 NeedsCompilation: yes Title: FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data Description: Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution. biocViews: RNASeq, SingleCell, Transcriptomics, DataImport, DifferentialSplicing, AlternativeSplicing, GeneExpression Author: Tian Luyi [aut], Voogd Oliver [aut, cre], Schuster Jakob [aut], Wang Changqing [aut], Su Shian [aut], Ritchie Matthew [ctb] Maintainer: Voogd Oliver URL: https://github.com/OliverVoogd/FLAMES SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FLAMES git_branch: RELEASE_3_15 git_last_commit: 2d5808d git_last_commit_date: 2022-08-06 Date/Publication: 2022-08-07 source.ver: src/contrib/FLAMES_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/FLAMES_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/FLAMES_1.2.2.tgz vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html vignetteTitles: FLAMES hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R dependencyCount: 205 Package: flowAI Version: 1.26.0 Depends: R (>= 3.6) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: 3b445503356f8b04d34013e374c84bec NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_15 git_last_commit: 194c329 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowAI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowAI_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowAI_1.26.0.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R dependencyCount: 74 Package: flowBeads Version: 1.34.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 32bf2ce0f43ca41e80b708b0e0cab532 NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: RELEASE_3_15 git_last_commit: 7f16b1e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowBeads_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowBeads_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowBeads_1.34.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 36 Package: flowBin Version: 1.32.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: b08b8033258759bf09b4da40fda2ad59 NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: RELEASE_3_15 git_last_commit: abeaaec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowBin_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowBin_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowBin_1.32.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 37 Package: flowcatchR Version: 1.30.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 0d43beeab54510f680290291126db5e9 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: RELEASE_3_15 git_last_commit: 4b767de git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowcatchR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowcatchR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowcatchR_1.30.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 97 Package: flowCHIC Version: 1.30.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 38182c815b9ab4a1409f5d494c1a3bfc NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: RELEASE_3_15 git_last_commit: 6ca8710 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCHIC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCHIC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCHIC_1.30.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 68 Package: flowCL Version: 1.34.0 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 54598fbacd0290417937fc9367c5ab0d NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry, ImmunoOncology Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas git_url: https://git.bioconductor.org/packages/flowCL git_branch: RELEASE_3_15 git_last_commit: e9626ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCL_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCL_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCL_1.34.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: flowClean Version: 1.34.1 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 872787dab9880ba1229c44cd902865cb NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: RELEASE_3_15 git_last_commit: 78fa18e git_last_commit_date: 2022-10-14 Date/Publication: 2022-10-16 source.ver: src/contrib/flowClean_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowClean_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/flowClean_1.34.1.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 25 Package: flowClust Version: 3.34.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto, flowStats(>= 4.7.1) License: Artistic-2.0 MD5sum: 016c0257bbea3b9052d6194fe8d3dab4 NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo, Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: RELEASE_3_15 git_last_commit: 60d0cb9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowClust_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowClust_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowClust_3.34.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: cyanoFilter, flowTrans suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 20 Package: flowCore Version: 2.8.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.3.4), S4Vectors LinkingTo: Rcpp, RcppArmadillo, BH(>= 1.65.0.1), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: x64 MD5sum: 9af5027ed9a780b41a6939b5449d99ec NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_15 git_last_commit: 586541d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCore_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCore_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCore_2.8.0.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, cyanoFilter, cydar, cytoMEM, CytoML, CytoTree, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowUtils, flowViz, flowWorkspace, GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, PeacoQC, scDataviz, Sconify suggestsMe: COMPASS, flowPloidyData, beadplexr, hypergate, segmenTier dependencyCount: 17 Package: flowCut Version: 1.6.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: e100ad0cfc1f5afa3b180f2fcf8a479c NeedsCompilation: no Title: Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: RELEASE_3_15 git_last_commit: c930af9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCut_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCut_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCut_1.6.0.tgz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 153 Package: flowCyBar Version: 1.32.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 3d02e13af51b389dbfb0d5f843d2a153 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: RELEASE_3_15 git_last_commit: cc134f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCyBar_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCyBar_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCyBar_1.32.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.30.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: 4f7654eaf7007d22b9076ba5a3a30a91 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: RELEASE_3_15 git_last_commit: eaee0fd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowDensity_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowDensity_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowDensity_1.30.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.html vignetteTitles: Introduction to automated gating hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 148 Package: flowFP Version: 1.54.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: 9441acbcb25ce00ec42dbb1bd00b0182 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_15 git_last_commit: c0366f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowFP_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowFP_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowFP_1.54.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 33 Package: flowGraph Version: 1.4.0 Depends: R (>= 4.1) Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2, igraph, Matrix, matrixStats, stats, utils, visNetwork, htmlwidgets, grDevices, methods, stringr, stringi, Rdpack, data.table (>= 1.9.5), gridExtra, Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: x64 MD5sum: 16f29874303163006c4b2e7a5054168d NeedsCompilation: no Title: Identifying differential cell populations in flow cytometry data accounting for marker frequency Description: Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts. biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software, CellBasedAssays, Visualization Author: Alice Yue [aut, cre] Maintainer: Alice Yue URL: https://github.com/aya49/flowGraph VignetteBuilder: knitr BugReports: https://github.com/aya49/flowGraph/issues git_url: https://git.bioconductor.org/packages/flowGraph git_branch: RELEASE_3_15 git_last_commit: fadfcc1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowGraph_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowGraph_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowGraph_1.4.0.tgz vignettes: vignettes/flowGraph/inst/doc/flowGraph.html vignetteTitles: flowGraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R dependencyCount: 67 Package: flowMap Version: 1.34.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 9dd5abc36edb0918ec531f1f4630f11d NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: ImmunoOncology, MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMap git_branch: RELEASE_3_15 git_last_commit: f94cab8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowMap_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMap_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMap_1.34.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R dependencyCount: 37 Package: flowMatch Version: 1.32.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 57c8ab3eb492faa3b54a7b0d4484c9d2 NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: RELEASE_3_15 git_last_commit: 66af1b9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowMatch_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMatch_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMatch_1.32.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 18 Package: flowMeans Version: 1.56.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 Archs: x64 MD5sum: dcbd882fc493e44fbb205b9106fdac91 NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: RELEASE_3_15 git_last_commit: a4f03a7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowMeans_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMeans_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMeans_1.56.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 40 Package: flowMerge Version: 2.44.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 082155376d6aceb98e46a552f6b6ea2f NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: RELEASE_3_15 git_last_commit: c6f6bf9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowMerge_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMerge_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMerge_2.44.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 48 Package: flowPeaks Version: 1.42.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 MD5sum: 0ed08be86b49a4dcfb040c718c885955 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: RELEASE_3_15 git_last_commit: da9fd47 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowPeaks_1.42.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/flowPeaks_1.42.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust dependencyCount: 0 Package: flowPloidy Version: 1.22.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: 1138e1f20ef3a23a418e5dcd08f95020 NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: RELEASE_3_15 git_last_commit: 336f3e7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowPloidy_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowPloidy_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowPloidy_1.22.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 123 Package: flowPlots Version: 1.44.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 31c007c5c85b6db7a3fa282bb7bfda19 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: RELEASE_3_15 git_last_commit: 9a32cf8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowPlots_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowPlots_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowPlots_1.44.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowSOM Version: 2.4.0 Depends: R (>= 4.0), igraph Imports: stats, utils, BiocGenerics, colorRamps, ConsensusClusterPlus, CytoML, dplyr, flowCore, flowWorkspace, ggforce, ggnewscale, ggplot2, ggpointdensity, ggpubr, ggrepel, grDevices, magrittr, methods, pheatmap, RColorBrewer, rlang, Rtsne, tidyr, XML, scattermore Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 12157b0012f3b6b9e46a1b51e92dab87 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: RELEASE_3_15 git_last_commit: bf61833 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FlowSOM_2.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/FlowSOM_2.4.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, CytoTree, diffcyt suggestsMe: HDCytoData dependencyCount: 190 Package: flowSpecs Version: 1.10.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0), hexbin (>= 1.28.2) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: dc18e32a2a01da00bb5cb3e63988fb3d NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: RELEASE_3_15 git_last_commit: e734c25 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowSpecs_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowSpecs_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowSpecs_1.10.0.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 64 Package: flowStats Version: 4.8.2 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor, mnormt Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: e586eb8b37993393ac0e54db5c5704f5 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: RELEASE_3_15 git_last_commit: 4ebb765 git_last_commit_date: 2022-07-05 Date/Publication: 2022-07-12 source.ver: src/contrib/flowStats_4.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowStats_4.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/flowStats_4.8.2.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowClust, flowCore, flowTime, flowViz, ggcyto dependencyCount: 111 Package: flowTime Version: 1.20.0 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats, flowClust, openCyto, flowStats, ggcyto License: Artistic-2.0 MD5sum: 72d6b024e2f8ec4cf07e1f058e490ca6 NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: RELEASE_3_15 git_last_commit: 1322ea1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowTime_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowTime_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowTime_1.20.0.tgz vignettes: vignettes/flowTime/inst/doc/gating-vignette.html, vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R, vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 35 Package: flowTrans Version: 1.48.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 1c3a8908b06a2570288196e9b75c3dcc NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: RELEASE_3_15 git_last_commit: 1b84f91 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowTrans_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowTrans_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowTrans_1.48.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 36 Package: flowUtils Version: 1.59.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 MD5sum: 68efb4e1739a657152b1a5f4e2d7301d NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues git_url: https://git.bioconductor.org/packages/flowUtils git_branch: master git_last_commit: 94ef3ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowUtils_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowUtils_1.59.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowUtils_1.59.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R importsMe: CytoTree suggestsMe: gatingMLData dependencyCount: 22 Package: flowViz Version: 1.60.2 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat License: Artistic-2.0 MD5sum: 2671d187f00dd1daee744aad8ee8e654 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_15 git_last_commit: a508720 git_last_commit_date: 2022-06-30 Date/Publication: 2022-07-03 source.ver: src/contrib/flowViz_1.60.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowViz_1.60.2.zip mac.binary.ver: bin/macosx/contrib/4.2/flowViz_1.60.2.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 32 Package: flowVS Version: 1.28.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 44fb882735bcad368d5b7ffef10e530e NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: RELEASE_3_15 git_last_commit: 8cb6b20 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowVS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowVS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowVS_1.28.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 112 Package: flowWorkspace Version: 4.8.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.3.9), lattice, latticeExtra, XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, Rcpp, scales, matrixStats, RcppParallel, RProtoBufLib, digest, aws.s3, aws.signature, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: file LICENSE License_restricts_use: yes MD5sum: ec8490d7e63275639ff36b4519a055fe NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_15 git_last_commit: 8cb4b97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowWorkspace_4.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowWorkspace_4.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowWorkspace_4.8.0.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: ggcyto, highthroughputassays importsMe: CytoML, flowDensity, FlowSOM, flowStats, ImmuneSpaceR, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore linksToMe: CytoML dependencyCount: 80 Package: fmcsR Version: 1.38.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: a24226bc9bf9cb5b94758dfd763f087e NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: RELEASE_3_15 git_last_commit: 051d4a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fmcsR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fmcsR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fmcsR_1.38.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: chemodiv suggestsMe: ChemmineR, xnet dependencyCount: 63 Package: fmrs Version: 1.6.0 Depends: R (>= 4.1.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 Archs: x64 MD5sum: 13878f9bd54ab6e753aa687f6c3ea9ee NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Models Description: The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: RELEASE_3_15 git_last_commit: ffc98cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fmrs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fmrs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fmrs_1.6.0.tgz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R dependencyCount: 10 Package: fobitools Version: 1.4.0 Depends: R (>= 4.1) Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr, ontologyIndex, purrr, RecordLinkage, stringr, textclean, tictoc, tidygraph, tidyr, vroom Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr, metabolomicsWorkbenchR, POMA, rmarkdown, rvest, SummarizedExperiment, testthat (>= 2.3.2), tidyverse License: GPL-3 Archs: x64 MD5sum: 1006b63802e24e9b472b3a79cc122e5b NeedsCompilation: no Title: Tools For Manipulating FOBI Ontology Description: A set of tools for interacting with Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data. biocViews: MassSpectrometry, Metabolomics, Software, Visualization, BiomedicalInformatics, GraphAndNetwork, Annotation, Cheminformatics, Pathways, GeneSetEnrichment Author: Pol Castellano-Escuder [aut, cre] (), Cristina Andrés-Lacueva [aut] (), Alex Sánchez-Pla [aut] () Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/fobitools/ VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/fobitools/issues git_url: https://git.bioconductor.org/packages/fobitools git_branch: RELEASE_3_15 git_last_commit: b58e886 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/fobitools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fobitools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fobitools_1.4.0.tgz vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html, vignettes/fobitools/inst/doc/food_enrichment_analysis.html, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html vignetteTitles: Dietary text annotation, Simple food ORA, Use case ST000291, Use case ST000629 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R, vignettes/fobitools/inst/doc/food_enrichment_analysis.R, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R dependencyCount: 124 Package: FoldGO Version: 1.14.0 Depends: R (>= 4.0) Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), stats, methods Suggests: knitr, rmarkdown, devtools, kableExtra License: GPL-3 MD5sum: decdf72e0298983e0479e93104685bc8 NeedsCompilation: no Title: Package for Fold-specific GO Terms Recognition Description: FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms. biocViews: DifferentialExpression, GeneExpression, GO, Software Author: Daniil Wiebe [aut, cre] Maintainer: Daniil Wiebe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FoldGO git_branch: RELEASE_3_15 git_last_commit: 1186e09 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FoldGO_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FoldGO_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FoldGO_1.14.0.tgz vignettes: vignettes/FoldGO/inst/doc/vignette.html vignetteTitles: FoldGO: a tool for fold-change-specific functional enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FoldGO/inst/doc/vignette.R dependencyCount: 82 Package: FRASER Version: 1.8.1 Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, utils, VGAM LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE MD5sum: 035fa9ee4aa9843007f03eb8ebbb41d5 NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre], Ines Scheller [aut], Vicente Yepez [ctb], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FRASER git_branch: RELEASE_3_15 git_last_commit: 8cf91e1 git_last_commit_date: 2022-06-28 Date/Publication: 2022-06-30 source.ver: src/contrib/FRASER_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/FRASER_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/FRASER_1.8.1.tgz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Evens in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 172 Package: frenchFISH Version: 1.8.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: e7becd69ef0920cdf1abceb5315d9220 NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: RELEASE_3_15 git_last_commit: 899cf91 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/frenchFISH_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frenchFISH_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frenchFISH_1.8.0.tgz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 93 Package: FRGEpistasis Version: 1.32.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: e06ca7c5d9e8730680acef78e9f1e03b NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: RELEASE_3_15 git_last_commit: 8584b65 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FRGEpistasis_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FRGEpistasis_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FRGEpistasis_1.32.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 59 Package: frma Version: 1.48.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: f4fc785212e1a1202f1e40df2ccd868a NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: RELEASE_3_15 git_last_commit: 3c39bcf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/frma_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frma_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frma_1.48.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, antiProfilesData dependencyCount: 55 Package: frmaTools Version: 1.48.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 801492582f1fa749022ccb9cc109ca4f NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: RELEASE_3_15 git_last_commit: 73b9299 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/frmaTools_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frmaTools_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frmaTools_1.48.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 13 Package: FScanR Version: 1.6.0 Depends: R (>= 4.0) Imports: stats Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 29dffcf04c141caca3dd6f2e6e8be99a NeedsCompilation: no Title: Detect Programmed Ribosomal Frameshifting Events from mRNA/cDNA BLASTX Output Description: 'FScanR' identifies Programmed Ribosomal Frameshifting (PRF) events from BLASTX homolog sequence alignment between targeted genomic/cDNA/mRNA sequences against the peptide library of the same species or a close relative. The output by BLASTX or diamond BLASTX will be used as input of 'FScanR' and should be in a tabular format with 14 columns. For BLASTX, the output parameter should be: -outfmt '6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe sframe'. For diamond BLASTX, the output parameter should be: -outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe qframe. biocViews: Alignment, Annotation, Software Author: Xiao Chen [aut, cre] () Maintainer: Xiao Chen VignetteBuilder: knitr BugReports: https://github.com/seanchen607/FScanR/issues git_url: https://git.bioconductor.org/packages/FScanR git_branch: RELEASE_3_15 git_last_commit: 4642e42 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FScanR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FScanR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FScanR_1.6.0.tgz vignettes: vignettes/FScanR/inst/doc/FScanR.html vignetteTitles: FScanR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FScanR/inst/doc/FScanR.R dependencyCount: 1 Package: FunChIP Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 MD5sum: 3709615fbab4c7f5dab03c4f7a98d0bb NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi git_url: https://git.bioconductor.org/packages/FunChIP git_branch: RELEASE_3_15 git_last_commit: fa64a66 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/FunChIP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FunChIP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FunChIP_1.22.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R dependencyCount: 112 Package: funtooNorm Version: 1.20.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: e25c4d8cbf81b53115ea464cf71b6b2e NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_15 git_last_commit: cac6b4f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/funtooNorm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/funtooNorm_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/funtooNorm_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 145 Package: GA4GHclient Version: 1.20.0 Depends: R (>= 3.5.0), S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 960dedf7d58c03f80a0e296f04bc9bcc NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: RELEASE_3_15 git_last_commit: 22ed961 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GA4GHclient_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GA4GHclient_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GA4GHclient_1.20.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 99 Package: GA4GHshiny Version: 1.18.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 3573c4591dfaab62241dc0c6eef61126 NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: RELEASE_3_15 git_last_commit: c86a8b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GA4GHshiny_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GA4GHshiny_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GA4GHshiny_1.18.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 124 Package: gaga Version: 2.42.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: e4da3b72eca7b4a4468ff80d2946159f NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: RELEASE_3_15 git_last_commit: 322fbfd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gaga_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gaga_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gaga_2.42.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 16 Package: gage Version: 2.46.1 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) Archs: x64 MD5sum: c8e88b8b596602b43e915a63c5898720 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: RELEASE_3_15 git_last_commit: 3aea19c git_last_commit_date: 2022-08-27 Date/Publication: 2022-08-28 source.ver: src/contrib/gage_2.46.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/gage_2.46.1.zip mac.binary.ver: bin/macosx/contrib/4.2/gage_2.46.1.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA importsMe: exp2flux suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 47 Package: gaggle Version: 1.64.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 467b623e17e4c2263d3c561f6c915624 NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ git_url: https://git.bioconductor.org/packages/gaggle git_branch: RELEASE_3_15 git_last_commit: a3d5c88 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gaggle_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gaggle_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gaggle_1.64.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R dependencyCount: 9 Package: gaia Version: 2.39.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: aaac30ae2437e014686c3fa168344dc7 NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella git_url: https://git.bioconductor.org/packages/gaia git_branch: master git_last_commit: f8b876c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gaia_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gaia_2.39.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gaia_2.39.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R dependencyCount: 0 Package: GAPGOM Version: 1.11.0 Depends: R (>= 4.0) Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2 License: MIT + file LICENSE MD5sum: 5b518703b3dfe6000b825e4d2fb53759 NeedsCompilation: no Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics) Description: Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms. biocViews: GO, GeneExpression, GenePrediction Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs [aut] Maintainer: Rezvan Ehsani URL: https://github.com/Berghopper/GAPGOM/ VignetteBuilder: knitr BugReports: https://github.com/Berghopper/GAPGOM/issues/ git_url: https://git.bioconductor.org/packages/GAPGOM git_branch: master git_last_commit: b7987ae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GAPGOM_1.11.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/GAPGOM_1.11.0.tgz vignettes: vignettes/GAPGOM/inst/doc/benchmarks.html, vignettes/GAPGOM/inst/doc/GAPGOM.html vignetteTitles: Benchmarks and other GO similarity methods, An Introduction to GAPGOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GAPGOM/inst/doc/benchmarks.R, vignettes/GAPGOM/inst/doc/GAPGOM.R dependencyCount: 92 Package: GAprediction Version: 1.22.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: e31d12cd88a7e359a88e9da75c74b1ec NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: RELEASE_3_15 git_last_commit: 7ca060a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GAprediction_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GAprediction_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GAprediction_1.22.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 17 Package: garfield Version: 1.24.0 Suggests: knitr License: GPL-3 MD5sum: df020cadf46efef93f423565afbc0367 NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: RELEASE_3_15 git_last_commit: 03ed890 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/garfield_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/garfield_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/garfield_1.24.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.16.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: x64 MD5sum: 41cd8399d2b37411d278104d70bd3347 NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: RELEASE_3_15 git_last_commit: 83aa869 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GARS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GARS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GARS_1.16.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 254 Package: GateFinder Version: 1.16.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: eac510df432894d8440c661f05a028cc NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: RELEASE_3_15 git_last_commit: 5efa1c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GateFinder_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GateFinder_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GateFinder_1.16.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 41 Package: GBScleanR Version: 1.0.6 Depends: SeqArray Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp, RcppParallel, gdsfmt LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 2dc8afb5d745bffa8e29e54438641b70 NeedsCompilation: yes Title: Error correction tool for noisy genotyping by sequencing (GBS) data Description: GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel, Sequencing, QualityControl Author: Tomoyuki Furuta [aut, cre] () Maintainer: Tomoyuki Furuta URL: https://github.com/tomoyukif/GBScleanR SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/tomoyukif/GBScleanR/issues git_url: https://git.bioconductor.org/packages/GBScleanR git_branch: RELEASE_3_15 git_last_commit: b95e1b2 git_last_commit_date: 2022-10-17 Date/Publication: 2022-10-18 source.ver: src/contrib/GBScleanR_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/GBScleanR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GBScleanR_1.0.6.tgz vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html vignetteTitles: BasicUsageOfGBScleanR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R dependencyCount: 93 Package: gcapc Version: 1.20.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 Archs: x64 MD5sum: 831ab2337b4ac899429e3b163f5fb95a NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: RELEASE_3_15 git_last_commit: 2f2f856 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gcapc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcapc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gcapc_1.20.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R suggestsMe: epigraHMM dependencyCount: 48 Package: gcatest Version: 1.26.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 MD5sum: ea717ba8c0c92fdfc2d70faf1ddcfda7 NeedsCompilation: yes Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait. models. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: RELEASE_3_15 git_last_commit: f06d4e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gcatest_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcatest_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gcatest_1.26.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 3 Package: gCrisprTools Version: 2.2.2 Depends: R (>= 4.1) Imports: Biobase, limma, RobustRankAggreg, ggplot2, SummarizedExperiment, grid, rmarkdown, grDevices, graphics, methods, ComplexHeatmap, stats, utils, parallel Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea License: Artistic-2.0 Archs: x64 MD5sum: ca5cc1152e0d91efe929a3ad6e765fd8 NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: RELEASE_3_15 git_last_commit: 4de8008 git_last_commit_date: 2022-09-28 Date/Publication: 2022-09-29 source.ver: src/contrib/gCrisprTools_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/gCrisprTools_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/gCrisprTools_2.2.2.tgz vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Contrast_Comparisons_gCrisprTools, Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 90 Package: gcrma Version: 2.68.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: f57df28dcdd0170ad5f62fbb6026f6fa NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: RELEASE_3_15 git_last_commit: c14063f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gcrma_2.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcrma_2.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gcrma_2.68.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 24 Package: GCSConnection Version: 1.7.0 Depends: R (>= 4.0.0) Imports: Rcpp (>= 1.0.2), httr, googleAuthR, googleCloudStorageR, methods, jsonlite, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 0358ffc71f2bb56df0f75030114be2e6 NeedsCompilation: yes Title: Creating R Connection with Google Cloud Storage Description: Create R 'connection' objects to google cloud storage buckets using the Google REST interface. Both read and write connections are supported. The package also provides functions to view and manage files on Google Cloud. biocViews: Infrastructure Author: Jiefei Wang [cre] Maintainer: Jiefei Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSConnection git_branch: master git_last_commit: 492facb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GCSConnection_1.7.0.tar.gz vignettes: vignettes/GCSConnection/inst/doc/Introduction.html vignetteTitles: quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSConnection/inst/doc/Introduction.R suggestsMe: GCSFilesystem dependencyCount: 32 Package: GCSFilesystem Version: 1.6.0 Depends: R (>= 4.0.0) Imports: stats Suggests: testthat, knitr, rmarkdown, BiocStyle, GCSConnection License: GPL (>= 2) Archs: x64 MD5sum: 8d36d027fd7813f34f6344e2004b8586 NeedsCompilation: no Title: Mounting a Google Cloud bucket to a local directory Description: Mounting a Google Cloud bucket to a local directory. The files in the bucket can be viewed and read as if they are locally stored. For using the package, you need to install GCSDokan on Windows or gcsfuse on Linux and MacOs. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GCSDokan for Windows, gcsfuse for Linux and macOs VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/GCSFilesystem git_branch: RELEASE_3_15 git_last_commit: f0ae004 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GCSFilesystem_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GCSFilesystem_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GCSFilesystem_1.6.0.tgz vignettes: vignettes/GCSFilesystem/inst/doc/Quick-Start-Guide.html vignetteTitles: Quick-Start-Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 1 Package: GCSscore Version: 1.10.0 Depends: R (>= 3.6) Imports: BiocManager, Biobase, utils, methods, RSQLite, devtools, dplR, stringr, graphics, stats, affxparser, data.table Suggests: siggenes, GEOquery, R.utils License: GPL (>=3) MD5sum: 5cf16b09e38c0f654638867b1c19c057 NeedsCompilation: no Title: GCSscore: an R package for microarray analysis for Affymetrix/Thermo Fisher arrays Description: For differential expression analysis of 3'IVT and WT-style microarrays from Affymetrix/Thermo-Fisher. Based on S-score algorithm originally described by Zhang et al 2002. biocViews: DifferentialExpression, Microarray, OneChannel, ProprietaryPlatforms, DataImport Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles Maintainer: Guy M. Harris git_url: https://git.bioconductor.org/packages/GCSscore git_branch: RELEASE_3_15 git_last_commit: 113e771 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GCSscore_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GCSscore_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GCSscore_1.10.0.tgz vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R dependencyCount: 126 Package: GDCRNATools Version: 1.16.6 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: f7b1a331dd83fc41e1d4774fb4f0626a NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: RELEASE_3_15 git_last_commit: 4e2df58 git_last_commit_date: 2022-08-19 Date/Publication: 2022-08-21 source.ver: src/contrib/GDCRNATools_1.16.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/GDCRNATools_1.16.6.zip mac.binary.ver: bin/macosx/contrib/4.2/GDCRNATools_1.16.6.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 237 Package: GDSArray Version: 1.16.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: d7dd09917a4090cb702be151e305cc30 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: RELEASE_3_15 git_last_commit: 2cc5b86 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GDSArray_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GDSArray_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GDSArray_1.16.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R importsMe: CNVRanger, VariantExperiment suggestsMe: DelayedDataFrame dependencyCount: 29 Package: gdsfmt Version: 1.32.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: 938410df440481a12b1bddab82be501e NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_15 git_last_commit: 06f2097 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gdsfmt_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gdsfmt_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gdsfmt_1.32.0.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, SAIGEgds, SCArray, SeqArray, SNPRelate, Mega2R importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.22.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: fff17f0e4fe8e25f8033ae4140bf325d NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: RELEASE_3_15 git_last_commit: e65a5c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEM_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEM_1.22.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 37 Package: gemini Version: 1.10.0 Depends: R (>= 4.1.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 3038691663eaebf2d18b87ed38f129de NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: RELEASE_3_15 git_last_commit: 1932c56 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gemini_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gemini_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gemini_1.10.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 45 Package: genArise Version: 1.72.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: c58a551749cac54942dce9c7d257b3a8 NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: RELEASE_3_15 git_last_commit: 00eaea7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genArise_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genArise_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genArise_1.72.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: genbankr Version: 1.24.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 07aaeade0aa2a4f5bcca771e4e09b0d9 NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genbankr git_branch: RELEASE_3_15 git_last_commit: e4d05e1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genbankr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genbankr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genbankr_1.24.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R importsMe: PACVr dependencyCount: 99 Package: GeneAccord Version: 1.14.0 Depends: R (>= 3.5) Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat License: file LICENSE MD5sum: c0a29dcf57805564ca4cff803cd9f7c5 NeedsCompilation: no Title: Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients Description: A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations. biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation, SomaticMutation, FunctionalGenomics, Genetics, MathematicalBiology, SystemsBiology, FeatureExtraction, PatternLogic, Pathways Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel Maintainer: Ariane L. Moore URL: https://github.com/cbg-ethz/GeneAccord VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneAccord git_branch: RELEASE_3_15 git_last_commit: 45b3e1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneAccord_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneAccord_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneAccord_1.14.0.tgz vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html vignetteTitles: GeneAccord hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R dependencyCount: 146 Package: geneAttribution Version: 1.22.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: a1d4fdae452f51c4c79b33a9507bb30d NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: RELEASE_3_15 git_last_commit: bd28194 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneAttribution_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneAttribution_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneAttribution_1.22.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 98 Package: GeneBreak Version: 1.26.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 84befe93fb4cc07a189426edfd32455f NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: RELEASE_3_15 git_last_commit: e2a5412 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneBreak_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneBreak_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneBreak_1.26.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 49 Package: geneClassifiers Version: 1.20.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 191b2cf2274b03b01a3105256ceef106 NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] () Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: RELEASE_3_15 git_last_commit: b002dde git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneClassifiers_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneClassifiers_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneClassifiers_1.20.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 6 Package: GeneExpressionSignature Version: 1.42.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: e8a332f39a02b85dd801a33e140776d9 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb] Maintainer: Yang Cao URL: https://github.com/yiluheihei/GeneExpressionSignature VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/GeneExpressionSignature/issues/ git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: RELEASE_3_15 git_last_commit: 83ce388 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneExpressionSignature_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneExpressionSignature_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneExpressionSignature_1.42.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 6 Package: genefilter Version: 1.78.0 Imports: BiocGenerics, AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: 982948da9c888610ef9da6172189494a NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman, Vincent J. Carey, Wolfgang Huber, Florian Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_15 git_last_commit: 2f57438 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genefilter_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genefilter_1.78.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genefilter_1.78.0.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf vignetteTitles: 01 - Using the genefilter function to filter genes from a microarray dataset, 02 - How to find genes whose expression profile is similar to that of specified genes, 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: cellHTS2, CNTools, GeneMeta, sva, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, Category, cbaf, countsimQC, covRNA, DESeq2, DEXSeq, GISPA, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, NxtIRFcore, pcaExplorer, PECA, phenoTest, protGear, pwrEWAS, Ringo, spatialHeatmap, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, RNAinteractMAPK, CoNI, dGAselID, INCATome, MiDA, netgsa, oncoPredict suggestsMe: annotate, BioNet, categoryCompare, ClassifyR, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, SuperLearner dependencyCount: 53 Package: genefu Version: 2.28.0 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 1d45c6f0536d1a610587c1264383cc9c NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut], Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker [aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: RELEASE_3_15 git_last_commit: f2f0027 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genefu_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genefu_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genefu_2.28.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.html vignetteTitles: genefu: A Package For Breast Cancer Gene Expression Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV, PDATK suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 110 Package: GeneGA Version: 1.46.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: cefd73fa542da73f5ff1489585c2b268 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: RELEASE_3_15 git_last_commit: 752a6f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneGA_1.46.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/GeneGA_1.46.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 15 Package: GeneGeneInteR Version: 1.22.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 9e4d904ec64dddcaa8bc60fbb702e326 NeedsCompilation: yes Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. . biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut] Maintainer: Mathieu Emily git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: RELEASE_3_15 git_last_commit: 000b1be git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneGeneInteR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneGeneInteR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneGeneInteR_1.22.0.tgz vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R dependencyCount: 136 Package: GeneMeta Version: 1.68.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 Archs: x64 MD5sum: 4852fbd6f1b1c14d86851a9ab37303c9 NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: RELEASE_3_15 git_last_commit: 4213c02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneMeta_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneMeta_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneMeta_1.68.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 54 Package: GeneNetworkBuilder Version: 1.38.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb, BiocStyle, magick, rmarkdown License: GPL (>= 2) MD5sum: b07c91144667db755fe11f0ecfce415a NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: RELEASE_3_15 git_last_commit: 1b30490 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneNetworkBuilder_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneNetworkBuilder_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneNetworkBuilder_1.38.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R dependencyCount: 22 Package: GeneOverlap Version: 1.32.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: a968a2c53375871c4137cffd9fabf22c NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: RELEASE_3_15 git_last_commit: 6f92a0e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneOverlap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneOverlap_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneOverlap_1.32.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.22.0 Depends: R (>= 4.0), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: b7a7868eda8b86baa1134c9414c7359e NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_15 git_last_commit: 609aaa3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneplast_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneplast_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneplast_1.22.0.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary rooting and plasticity analysis." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R suggestsMe: TreeAndLeaf, geneplast.data dependencyCount: 19 Package: geneplotter Version: 1.74.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 MD5sum: d2467109ac0babeceed8a63d4d61098d NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_15 git_last_commit: ca81956 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneplotter_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneplotter_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneplotter_1.74.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, Hiiragi2013, maEndToEnd importsMe: biocGraph, DESeq2, DEXSeq, IsoGeneGUI, MethylSeekR, RNAinteract suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 51 Package: geneRecommender Version: 1.68.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 6e59b1071f9719302ae63dc574e74ec5 NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: RELEASE_3_15 git_last_commit: 430dcc5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneRecommender_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneRecommender_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneRecommender_1.68.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 6 Package: GeneRegionScan Version: 1.52.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 82b285ef7845d073a1072d11a8914abc NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: RELEASE_3_15 git_last_commit: 9fc2a3f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneRegionScan_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneRegionScan_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneRegionScan_1.52.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 21 Package: geneRxCluster Version: 1.32.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: ddc12699b0a2919a7d90d69cdc9b00dc NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: RELEASE_3_15 git_last_commit: 8ec68d0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneRxCluster_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneRxCluster_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneRxCluster_1.32.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 16 Package: GeneSelectMMD Version: 2.40.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) Archs: x64 MD5sum: 6cdd72bbe0bf8e7ea14b38a3882db717 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: RELEASE_3_15 git_last_commit: 0adb1bb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneSelectMMD_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneSelectMMD_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneSelectMMD_2.40.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 9 Package: GENESIS Version: 2.26.0 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: x64 MD5sum: 05d23e887c6b241e1ed84e7bf4f568b8 NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_15 git_last_commit: 1cffe35 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GENESIS_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GENESIS_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GENESIS_2.26.0.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R dependencyCount: 104 Package: GeneStructureTools Version: 1.16.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 7772503edb24bb7e2426b155a91f901f NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: RELEASE_3_15 git_last_commit: 25bbb44 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneStructureTools_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneStructureTools_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneStructureTools_1.16.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 149 Package: geNetClassifier Version: 1.36.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 6fa1e77e0cc34fa2666d7e2d5c587d79 NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: RELEASE_3_15 git_last_commit: b5b4a80 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geNetClassifier_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geNetClassifier_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geNetClassifier_1.36.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 17 Package: GeneticsPed Version: 1.58.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 0959ef64a51a1226d3e11023b19633cb NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: RELEASE_3_15 git_last_commit: 3f50b5b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneticsPed_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneticsPed_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneticsPed_1.58.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R importsMe: LRQMM dependencyCount: 11 Package: GeneTonic Version: 2.0.2 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, ComplexUpset, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2, ggrepel, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinyAce, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: 6ed9de63c115764a0fe1585b023abcc1 NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides a Shiny application that aims to combine at different levels the existing pieces of the transcriptome data and results, in a way that makes it easier to generate insightful observations and hypothesis - combining the benefits of interactivity and reproducibility, e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO Author: Federico Marini [aut, cre] (), Annekathrin Ludt [aut] () Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues git_url: https://git.bioconductor.org/packages/GeneTonic git_branch: RELEASE_3_15 git_last_commit: 19e79ae git_last_commit_date: 2022-08-03 Date/Publication: 2022-08-07 source.ver: src/contrib/GeneTonic_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneTonic_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneTonic_2.0.2.tgz vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html vignetteTitles: The GeneTonic User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R dependencyCount: 171 Package: geneXtendeR Version: 1.22.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) MD5sum: 2ac9660e9d1625591ff96502f7ce3759 NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: RELEASE_3_15 git_last_commit: 36e8d12 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geneXtendeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneXtendeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneXtendeR_1.22.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 113 Package: GENIE3 Version: 1.18.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) Archs: x64 MD5sum: 4cf408c99fad63aac6384f80b65fc83a NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: RELEASE_3_15 git_last_commit: f16b25e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GENIE3_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GENIE3_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GENIE3_1.18.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, bulkAnalyseR suggestsMe: dnapath dependencyCount: 27 Package: genoCN Version: 1.48.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: x64 MD5sum: 5f0db72d8153350357a45d2e968c7595 NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun git_url: https://git.bioconductor.org/packages/genoCN git_branch: RELEASE_3_15 git_last_commit: df55a61 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genoCN_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genoCN_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genoCN_1.48.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R dependencyCount: 3 Package: genomation Version: 1.28.0 Depends: R (>= 3.5.0), grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 9844a84bf618e2ff8143b80afc77e7d9 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: RELEASE_3_15 git_last_commit: 4ef945a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genomation_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomation_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomation_1.28.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, EpiCompare, fCCAC, RCAS suggestsMe: methylKit dependencyCount: 97 Package: GenomeInfoDb Version: 1.32.4 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.25.12), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: ea2f1b1fd9237a45254daf13e4ef6298 NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: RELEASE_3_15 git_last_commit: 69df6a5 git_last_commit_date: 2022-09-06 Date/Publication: 2022-09-06 source.ver: src/contrib/GenomeInfoDb_1.32.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomeInfoDb_1.32.4.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomeInfoDb_1.32.4.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: Biostrings, BRGenomics, BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, IdeoViz, Rsamtools, SCOPE, VariantAnnotation, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, UCSCRepeatMasker, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, RTIGER importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, atena, BaalChIP, ballgown, bambu, BioPlex, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr, casper, cBioPortalData, CexoR, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito, comapr, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, crisprBowtie, crisprBwa, CRISPRseek, CrispRVariants, csaw, customProDB, DAMEfinder, dasper, decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, ensemblVEP, epialleleR, epigenomix, epigraHMM, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, extraChIPs, FindIT2, FLAMES, FRASER, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, genomation, genomeIntervals, GenomicDistributions, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicScores, genotypeeval, GenVisR, ggbio, gmoviz, GOTHiC, GRaNIE, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiCBricks, HiTC, HTSeqGenie, idr2d, IMAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene, metagene2, metaseqR2, metavizr, methimpute, methInheritSim, methylKit, methylPipe, methylSig, methylumi, minfi, MinimumDistance, MMAPPR2, monaLisa, mosaics, Motif2Site, motifbreakR, motifmatchr, MouseFM, msgbsR, multicrispr, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, OGRE, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteoDisco, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rcade, RCAS, RcisTarget, recount, recoup, regioneR, regionReport, REMP, Repitools, rfPred, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RLSeq, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scanMiR, scanMiRApp, scDblFinder, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqsetvis, sesame, sevenC, SGSeq, ShortRead, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, spiky, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, TAPseq, TarSeqQC, TCGAutils, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TVTB, tximeta, Ularcirc, UMI4Cats, VanillaICE, VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr, YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, GenomicDistributionsData, MethylSeqData, sesameData, TCGAWorkflow, ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR, ggcoverage, HiCfeat, ICAMS, intePareto, MAAPER, Mega2R, Signac, simMP, xQTLbiolinks suggestsMe: AnnotationForge, AnnotationHub, BindingSiteFinder, BiocOncoTK, chromswitch, epimutacions, ExperimentHubData, fishpond, megadepth, methrix, parglms, plotgardener, QDNAseq, scTreeViz, splatter, systemPipeR, TFutils, xcoredata, gkmSVM, LDheatmap, polyRAD, Seurat dependencyCount: 11 Package: genomeIntervals Version: 1.52.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 MD5sum: 202b0dd02ed7eae643e38d1082236cad NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: RELEASE_3_15 git_last_commit: 1e233dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genomeIntervals_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomeIntervals_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomeIntervals_1.52.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe, ChIC.data importsMe: ChIC, easyRNASeq dependencyCount: 17 Package: genomes Version: 3.26.0 Depends: readr, curl License: GPL-3 MD5sum: d00a1cef5a73ef396a7b47574e321e0c NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: RELEASE_3_15 git_last_commit: e669c95 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genomes_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomes_3.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomes_3.26.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 32 Package: GenomicAlignments Version: 1.32.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle License: Artistic-2.0 MD5sum: f52948ea89d1b6c9bf9a1513e9406114 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicAlignments Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_15 git_last_commit: 2553580 git_last_commit_date: 2022-07-21 Date/Publication: 2022-07-24 source.ver: src/contrib/GenomicAlignments_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicAlignments_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicAlignments_1.32.1.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, groHMM, HelloRanges, hiReadsProcessor, igvR, ORFik, prebs, recoup, RiboDiPA, ShortRead, SplicingGraphs, alpineData, SCATEData, sequencing importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC, atena, BaalChIP, bambu, biovizBase, breakpointR, BRGenomics, CAGEfightR, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, easyRNASeq, esATAC, FRASER, FunChIP, gcapc, genomation, GenomicFiles, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, ramwas, Rcade, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, SCATE, scruff, seqsetvis, SGSeq, soGGi, spiky, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq, TarSeqQC, TCseq, trackViewer, transcriptR, Ularcirc, UMI4Cats, VaSP, VplotR, leeBamViews, alakazam, ggcoverage, intePareto, MAAPER, PACVr, pulseTD, VALERIE suggestsMe: amplican, BindingSiteFinder, BiocParallel, csaw, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR, alpineData, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 38 Package: GenomicDataCommons Version: 1.20.3 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools, R.utils, data.table License: Artistic-2.0 Archs: x64 MD5sum: 17731e25a4155421372c5e92719266d5 NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_15 git_last_commit: 7440323 git_last_commit_date: 2022-10-07 Date/Publication: 2022-10-09 source.ver: src/contrib/GenomicDataCommons_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicDataCommons_1.20.3.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicDataCommons_1.20.3.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons, Questions and answers from over the years, Somatic Mutation Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R importsMe: GDCRNATools, TCGAutils dependencyCount: 54 Package: GenomicDistributions Version: 1.4.6 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, scales, broom, GenomeInfoDb, stats Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown, GenomicDistributionsData Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: fb79edc8e353e98c2ec7af6bf5f746f7 NeedsCompilation: no Title: GenomicDistributions: fast analysis of genomic intervals with Bioconductor Description: If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another. biocViews: Software, GenomeAnnotation, GenomeAssembly, DataRepresentation, Sequencing, Coverage, FunctionalGenomics, Visualization Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [aut], John Lawson [aut], Jose Verdezoto [aut], Michal Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut] Maintainer: Kristyna Kupkova URL: http://code.databio.org/GenomicDistributions VignetteBuilder: knitr BugReports: http://github.com/databio/GenomicDistributions git_url: https://git.bioconductor.org/packages/GenomicDistributions git_branch: RELEASE_3_15 git_last_commit: 78a0d8f git_last_commit_date: 2022-07-23 Date/Publication: 2022-07-24 source.ver: src/contrib/GenomicDistributions_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicDistributions_1.4.6.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicDistributions_1.4.6.tgz vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html, vignettes/GenomicDistributions/inst/doc/intro.html vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started with GenomicDistributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R dependencyCount: 65 Package: GenomicFeatures Version: 1.48.4 Depends: R (>= 3.5.0), BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.25.7), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), BiocIO, rtracklayer (>= 1.51.5), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, AnnotationFilter, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: e3332b86b772f8a382e0bc1381063fa9 NeedsCompilation: no Title: Conveniently import and query gene models Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence, V. Obenchain Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_15 git_last_commit: 06e37dc git_last_commit_date: 2022-09-19 Date/Publication: 2022-09-20 source.ver: src/contrib/GenomicFeatures_1.48.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicFeatures_1.48.4.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicFeatures_1.48.4.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: Cogito, cpvSNP, ensembldb, GSReg, Guitar, HelloRanges, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpediaFI, ASpli, bambu, BgeeCall, BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, consensusDE, crisprseekplus, CSSQ, customProDB, dasper, decompTumor2Sig, derfinder, derfinderPlot, EDASeq, ELMER, epimutacions, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, FindIT2, FLAMES, FRASER, GA4GHshiny, genbankr, geneAttribution, GenomicInteractionNodes, GenVisR, ggbio, gmapR, gmoviz, GUIDEseq, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, InPAS, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, metaseqR2, methylumi, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, RCAS, recoup, Rhisat2, RiboCrypt, RiboProfiling, ribosomeProfilingQC, RITAN, RLSeq, RNAmodR, scanMiRApp, scruff, SGSeq, sitadela, spatzie, SplicingGraphs, SPLINTER, srnadiff, StructuralVariantAnnotation, svaNUMT, svaRetro, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, TRESS, txcutr, tximeta, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis, DMRcatedata, geneLenDataBase, GenomicDistributionsData, scRNAseq, driveR, MAAPER, oncoPredict, pulseTD, utr.annotation suggestsMe: AnnotationHub, BANDITS, biomvRCNS, BioPlex, Biostrings, chipseq, chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR, fishpond, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, ODER, pageRank, plotgardener, recount, RNAmodR.ML, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, systemPipeR, TFutils, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, curatedAdipoChIP, ObMiTi, parathyroidSE, Single.mTEC.Transcriptomes, systemPipeRdata, CAGEWorkflow, polyRAD dependencyCount: 96 Package: GenomicFiles Version: 1.32.1 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), MatrixGenerics, GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation (>= 1.27.9), GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: 136282500764ce57ad5054ba24bad27b NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_15 git_last_commit: abe840c git_last_commit_date: 2022-05-06 Date/Publication: 2022-05-15 source.ver: src/contrib/GenomicFiles_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicFiles_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicFiles_1.32.1.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, ldblock, QuasR, Rqc, VCFArray suggestsMe: TFutils dependencyCount: 99 Package: genomicInstability Version: 1.2.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: eae22efeef71ac36f8624894974ed348 NeedsCompilation: no Title: Genomic Instability estimation for scRNA-Seq Description: This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell. biocViews: SystemsBiology, GeneExpression, SingleCell Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth [cph] Maintainer: Mariano Alvarez URL: https://github.com/DarwinHealth/genomicInstability BugReports: https://github.com/DarwinHealth/genomicInstability git_url: https://git.bioconductor.org/packages/genomicInstability git_branch: RELEASE_3_15 git_last_commit: 7638b24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genomicInstability_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomicInstability_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomicInstability_1.2.0.tgz vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf vignetteTitles: Using genomicInstability hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R dependencyCount: 34 Package: GenomicInteractionNodes Version: 1.0.0 Depends: R (>= 4.2.0), stats Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures, GenomeInfoDb, methods, IRanges, RBGL, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: file LICENSE MD5sum: 0a2a769f1ec537c759048f2db24f2f70 NeedsCompilation: no Title: A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data Description: The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation. biocViews: HiC, Sequencing, Software Author: Jianhong Ou [aut, cre], Yarui Diao [fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GenomicInteractionNodes VignetteBuilder: knitr BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes git_branch: RELEASE_3_15 git_last_commit: 1f9082e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicInteractionNodes_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicInteractionNodes_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicInteractionNodes_1.0.0.tgz vignettes: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html vignetteTitles: GenomicInteractionNodes Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R dependencyCount: 100 Package: GenomicInteractions Version: 1.30.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 96659ea221c4f64b249228789fd78fca NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: RELEASE_3_15 git_last_commit: cc79eb1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicInteractions_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicInteractions_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicInteractions_1.30.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, extraChIPs, spatzie suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 148 Package: GenomicOZone Version: 1.10.0 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) MD5sum: d964f75c230541af7be2fecf606cbc90 NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: RELEASE_3_15 git_last_commit: 887a7cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicOZone_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicOZone_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicOZone_1.10.0.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 161 Package: GenomicRanges Version: 1.48.0 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.15.2) Imports: utils, stats, XVector (>= 0.29.2) LinkingTo: S4Vectors, IRanges Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 98398fd94d284583e784608cb404c7cb NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_15 git_last_commit: 2bce608 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicRanges_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicRanges_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicRanges_1.48.0.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, baySeq, BindingSiteFinder, biomvRCNS, BiSeq, bnbc, BPRMeth, breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chromPlot, chromstaR, chromswitch, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, Cogito, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet, esATAC, ExCluster, exomeCopy, extraChIPs, fastseg, fCCAC, FindIT2, FunChIP, GeneBreak, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores, GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HiTC, IdeoViz, igvR, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, m6Aboost, maser, MBASED, Melissa, metagene, metagene2, methimpute, methylKit, methylPipe, minfi, MotifDb, msgbsR, MutationalPatterns, NADfinder, ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, ramr, Rcade, recoup, regioneR, RepViz, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, SCOPE, segmentSeq, seqbias, seqCAT, SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, spiky, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, TarSeqQC, TnT, trackViewer, TransView, traseR, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VarCon, VariantAnnotation, VariantTools, VplotR, vtpnet, vulcan, wavClusteR, YAPSA, EuPathDB, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, ChAMPdata, EatonEtAlChIPseq, nullrangesData, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver, sequencing, HiCfeat, PlasmaMutationDetector, PlasmaMutationDetector2, RTIGER importsMe: ACE, ALDEx2, alpine, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, atena, BadRegionFinder, ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, BioPlex, BioTIP, biovizBase, biscuiteer, BiSeq, BOBaFIT, borealis, brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse, cageminer, CAGEr, cBioPortalData, CexoR, ChIC, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, cliProfiler, CNEr, CNVfilteR, CNViz, CNVMetrics, comapr, coMET, coMethDMR, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprBase, crisprBowtie, CRISPRseek, crisprseekplus, CrispRVariants, customProDB, DAMEfinder, dasper, debrowser, decompTumor2Sig, deconvR, DeepBlueR, DEFormats, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffloop, diffUTR, DMRcate, dmrseq, DominoEffect, DRIMSeq, DropletUtils, easyRNASeq, EDASeq, eisaR, ELMER, enhancerHomologSearch, enrichTF, epialleleR, EpiCompare, epidecodeR, epigraHMM, epimutacions, epistack, EpiTxDb, epivizr, epivizrData, erma, EventPointer, fcScan, FilterFFPE, fishpond, FLAMES, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractionNodes, GenomicInteractions, genotypeeval, GenVisR, ggbio, GOfuncR, gpart, GRaNIE, gwascat, h5vc, heatmaps, hermes, HiCBricks, HiCcompare, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS, karyoploteR, loci2path, LOLA, LoomExperiment, lumi, MACPET, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2, methInheritSim, MethReg, methrix, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, mitoClone2, MMAPPR2, MMDiff2, Modstrings, monaLisa, mosaics, Motif2Site, motifbreakR, motifmatchr, MouseFM, MSA2dist, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, NanoMethViz, nanotatoR, ncRNAtools, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, OGRE, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, pageRank, panelcn.mops, PAST, pcaExplorer, pepStat, PhIPData, Pi, PICS, PING, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, RcisTarget, recount, recount3, regioneR, regionReport, regutools, REMP, Repitools, rfPred, rGADEM, RGMQL, Rhisat2, RiboCrypt, RiboDiPA, RiboProfiling, RIPAT, RLSeq, Rmmquant, rmspc, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, RTCGAToolbox, scanMiR, scanMiRApp, SCATE, scDblFinder, scmeth, scoreInvHap, scPipe, scruff, scuttle, segmenter, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, snapcount, soGGi, SparseSignatures, spatzie, SpectralTAD, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, terraTCGAdata, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, TitanCNA, tLOH, tracktables, transcriptR, transite, trena, TRESS, tricycle, triplex, tscR, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering, VaSP, VCFArray, wiggleplotr, xcore, XNAString, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, COSMIC.67, ELMER.data, GenomicDistributionsData, leeBamViews, MethylSeqData, pepDat, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, VariantToolsData, recountWorkflow, TCGAWorkflow, ActiveDriverWGS, cinaR, crispRdesignR, driveR, geneHapR, geno2proteo, ggcoverage, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, MitoHEAR, noisyr, numbat, oncoPredict, PACVr, pagoo, RapidoPGS, scPloidy, Signac, simMP, SNPassoc, utr.annotation, VALERIE, xQTLbiolinks suggestsMe: AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, Chicago, CNVgears, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, maftools, MiRaGE, omicsPrint, parglms, plotgardener, recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, cancerTiming, chicane, DGEobj, gkmSVM, LDheatmap, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, updog, valr dependencyCount: 15 Package: GenomicScores Version: 2.8.2 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard License: Artistic-2.0 Archs: x64 MD5sum: e152a0edfcad2975796dafe24ee7c9df NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage, AnnotationHubSoftware Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: RELEASE_3_15 git_last_commit: 4c823c4 git_last_commit_date: 2022-06-15 Date/Publication: 2022-06-21 source.ver: src/contrib/GenomicScores_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicScores_2.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicScores_2.8.2.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons7way.UCSC.hg38 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix, SNPassoc dependencyCount: 98 Package: GenomicSuperSignature Version: 1.4.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis, BiocStyle, testthat, forcats, stats, wordcloud, circlize, EnrichmentBrowser, clusterProfiler, msigdbr, cluster, RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr, utils License: Artistic-2.0 MD5sum: 38aa4dcea080b0ab5b569ff284b27e97 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package. biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq, Sequencing, Pathways, Clustering Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/GenomicSuperSignature VignetteBuilder: knitr BugReports: https://github.com/shbrief/GenomicSuperSignature/issues git_url: https://git.bioconductor.org/packages/GenomicSuperSignature git_branch: RELEASE_3_15 git_last_commit: 56c08bb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicSuperSignature_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicSuperSignature_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicSuperSignature_1.4.0.tgz vignettes: vignettes/GenomicSuperSignature/inst/doc/GenomicSuperSignature_Contents.html, vignettes/GenomicSuperSignature/inst/doc/Quickstart.html vignetteTitles: Introduction on RAVmodel, Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicSuperSignature/inst/doc/GenomicSuperSignature_Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 167 Package: GenomicTuples Version: 1.30.0 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, covr License: Artistic-2.0 MD5sum: b8a53c37b525dc2fe9cceb7c44cdbf05 NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: RELEASE_3_15 git_last_commit: 7b2840d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenomicTuples_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicTuples_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicTuples_1.30.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 18 Package: genotypeeval Version: 1.28.0 Depends: R (>= 3.4.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE Archs: x64 MD5sum: b6aea54dff2611236c0c439ce54bc4e8 NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/genotypeeval git_branch: RELEASE_3_15 git_last_commit: 7ffec7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genotypeeval_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genotypeeval_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genotypeeval_1.28.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 113 Package: genphen Version: 1.24.0 Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats, graphics Imports: rstan (>= 2.17.3), ranger, parallel, foreach, doParallel, e1071, Biostrings, rPref Suggests: testthat, ggplot2, gridExtra, ape, ggrepel, knitr, reshape, xtable License: GPL (>= 2) MD5sum: d6019b69c0ce4be37102c0883b9a0f05 NeedsCompilation: no Title: genphen: tool for quantification of genotype-phenotype associations in genome wide association studies (GWAS) Description: Genetic association studies help us discover relationships between genotypes and phenotype. genphen is a computational tool for quantification of genotype-phenotype associations using a hybrid approach based on statistical learning techniques and probabilistic models that are analyzed computationally by Bayes inference. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski BugReports: https://github.com/snaketron/genphen/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/genphen git_branch: RELEASE_3_15 git_last_commit: 2158d7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/genphen_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/genphen_1.24.0.tgz vignettes: vignettes/genphen/inst/doc/genphenManual.pdf vignetteTitles: genphen overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genphen/inst/doc/genphenManual.R dependencyCount: 83 Package: GenProSeq Version: 1.0.0 Depends: keras, mclust, R (>= 4.2) Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: f323cda677d611e3d2b2476d213996ed NeedsCompilation: no Title: Generating Protein Sequences with Deep Generative Models Description: Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model. biocViews: Software, Proteomics Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: RELEASE_3_15 git_last_commit: 599abac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenProSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenProSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenProSeq_1.0.0.tgz vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html vignetteTitles: GenProSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R dependencyCount: 149 Package: GenVisR Version: 1.28.0 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 8afd8998254318028486d376a78f8aac NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: RELEASE_3_15 git_last_commit: c6c6b57 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GenVisR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenVisR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenVisR_1.28.0.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 120 Package: GeoDiff Version: 1.2.0 Depends: R (>= 4.1.0), Biobase Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods, graphics, stats, testthat, GeomxTools, NanoStringNCTools LinkingTo: Rcpp, RcppArmadillo, roptim Suggests: knitr, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 2d139bfd2c138aec70c12486fd13dd1d NeedsCompilation: yes Title: Count model based differential expression and normalization on GeoMx RNA data Description: A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette. biocViews: GeneExpression, DifferentialExpression, Normalization Author: Nicole Ortogero [cre], Lei Yang [aut], Zhi Yang [aut] Maintainer: Nicole Ortogero URL: https://github.com/Nanostring-Biostats/GeoDiff VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/GeoDiff git_url: https://git.bioconductor.org/packages/GeoDiff git_branch: RELEASE_3_15 git_last_commit: 1197c97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeoDiff_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeoDiff_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeoDiff_1.2.0.tgz vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html vignetteTitles: Workflow_WTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R dependencyCount: 132 Package: GEOexplorer Version: 1.2.0 Depends: shiny, limma, Biobase, plotly, shinyBS Imports: DT, htmltools, factoextra, heatmaply, maptools, pheatmap, scales, shinyHeatmaply, shinybusy, ggplot2, stringr, umap, GEOquery, impute, grDevices, stats, graphics, utils Suggests: rmarkdown, knitr, usethis, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 23b93d2fe7133e04920b29ef1dac8228 NeedsCompilation: no Title: GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation Description: GEOexplorer is a Shiny app that enables exploratory data analysis and differential gene expression of gene expression analysis on microarray gene expression datasets held on the GEO database. The outputs are interactive graphs that enable users to explore the results of the analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/). biocViews: Software, GeneExpression, mRNAMicroarray, DifferentialExpression, Microarray, MicroRNAArray Author: Guy Hunt [aut, cre] (), Rafael Henkin [ctb, ths] (), Fabrizio Smeraldi [ctb, ths] (), Michael Barnes [ctb, ths] () Maintainer: Guy Hunt URL: https://github.com/guypwhunt/GEOexplorer/ VignetteBuilder: knitr BugReports: https://github.com/guypwhunt/GEOexplorer/issues git_url: https://git.bioconductor.org/packages/GEOexplorer git_branch: RELEASE_3_15 git_last_commit: 3158786 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEOexplorer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOexplorer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOexplorer_1.2.0.tgz vignettes: vignettes/GEOexplorer/inst/doc/GEOexplorer.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOexplorer/inst/doc/GEOexplorer.R dependencyCount: 187 Package: GEOfastq Version: 1.4.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: d4f7f2e72e590e1941ad8750aa0751c3 NeedsCompilation: no Title: Downloads ENA Fastqs With GEO Accessions Description: GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA. biocViews: RNASeq, DataImport Author: Alex Pickering [cre, aut] () Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: RELEASE_3_15 git_last_commit: 47a1044 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEOfastq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOfastq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOfastq_1.4.0.tgz vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html vignetteTitles: Using the GEOfastq Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R dependencyCount: 40 Package: GEOmetadb Version: 1.58.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: b01bc85038d7231ec1b530be0b6eff11 NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_15 git_last_commit: 01519ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEOmetadb_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOmetadb_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOmetadb_1.58.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R importsMe: MetaIntegrator suggestsMe: antiProfilesData, maGUI dependencyCount: 54 Package: GeomxTools Version: 3.0.1 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, lmerTest, dplyr, stringr, grDevices, graphics, GGally, rlang, ggplot2, SeuratObject Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph, Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork License: MIT MD5sum: bccdc890b550c256a304e00a74e306ce NeedsCompilation: no Title: NanoString GeoMx Tools Description: Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign, Normalization, Spatial Author: Nicole Ortogero [cre, aut], Zhi Yang [aut], Ronalyn Vitancol [aut], Maddy Griswold [aut], David Henderson [aut] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: RELEASE_3_15 git_last_commit: 2c91b92 git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-29 source.ver: src/contrib/GeomxTools_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeomxTools_3.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GeomxTools_3.0.1.tgz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet, Coercion of GeoMxSet to Seurat and SpatialExperiment Objects, Protein data using GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon dependencyCount: 122 Package: GEOquery Version: 2.64.2 Depends: methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, R.utils Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown License: MIT MD5sum: f80c0f9b7693139970288ebc927e7e04 NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis [aut, cre] () Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery, http://seandavi.github.io/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_15 git_last_commit: e9b7f07 git_last_commit_date: 2022-05-14 Date/Publication: 2022-05-15 source.ver: src/contrib/GEOquery_2.64.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOquery_2.64.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOquery_2.64.2.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, BioPlex, ChIPXpress, conclus, crossmeta, DExMA, EGAD, GAPGOM, GEOexplorer, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb, BeadArrayUseCases, GSE13015, healthyControlsPresenceChecker, easyDifferentialGeneCoexpression, geneExpressionFromGEO, MetaIntegrator, seeker suggestsMe: AUCell, autonomics, ctsGE, dearseq, debCAM, diffcoexp, dyebias, EpiDISH, fgsea, GCSscore, GeneExpressionSignature, GenomicOZone, methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools, quantiseqr, RegEnrich, RGSEA, Rnits, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, airway, antiProfilesData, muscData, parathyroidSE, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray dependencyCount: 45 Package: GEOsubmission Version: 1.48.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: a9c448edc7e5c07c559451b5b3208752 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: RELEASE_3_15 git_last_commit: 2aea084 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEOsubmission_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOsubmission_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOsubmission_1.48.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 12 Package: gep2pep Version: 1.16.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: d1fe363e69608870e5c7e016bba21e11 NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: RELEASE_3_15 git_last_commit: 9661693 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gep2pep_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gep2pep_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gep2pep_1.16.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 58 Package: gespeR Version: 1.28.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: b395a193df1f4b3f8c7c1b5df8de45aa NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gespeR git_branch: RELEASE_3_15 git_last_commit: 89151b6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gespeR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gespeR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gespeR_1.28.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R dependencyCount: 117 Package: getDEE2 Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 6475790cc709cbf559de8691568c82df NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: RELEASE_3_15 git_last_commit: 6aef81b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/getDEE2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/getDEE2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/getDEE2_1.6.0.tgz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R dependencyCount: 26 Package: geva Version: 1.4.0 Depends: R (>= 4.1) Imports: grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 2bf2c9fa76e59ed2c78149bedab15cf4 NeedsCompilation: no Title: Gene Expression Variation Analysis (GEVA) Description: Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors. biocViews: Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, SystemsBiology, Transcriptomics Author: Itamar José Guimarães Nunes [aut, cre] (), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (), Marcio Dorn [ctb] () Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: RELEASE_3_15 git_last_commit: 15633b9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/geva_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geva_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geva_1.4.0.tgz vignettes: vignettes/geva/inst/doc/geva.pdf vignetteTitles: GEVA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geva/inst/doc/geva.R dependencyCount: 9 Package: GEWIST Version: 1.40.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 22664f0be770991dc4ce02d4bdc125db NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: RELEASE_3_15 git_last_commit: 587573d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GEWIST_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEWIST_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEWIST_1.40.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 88 Package: ggbio Version: 1.44.1 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: 135422bd8dd402a7ca54588f281ea549 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: https://lawremi.github.io/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/lawremi/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_15 git_last_commit: 0301d94 git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/ggbio_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggbio_1.44.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ggbio_1.44.1.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: BOBaFIT, cageminer, derfinderPlot, FLAMES, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures, ggcoverage suggestsMe: bambu, beadarray, ensembldb, gwascat, interactiveDisplay, NanoStringNCTools, Pi, regionReport, RnBeads, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 156 Package: ggcyto Version: 1.24.1 Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.33.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: Artistic-2.0 MD5sum: b311055f5173b8cd8874ef244b30a0e1 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_15 git_last_commit: 8e94519 git_last_commit_date: 2022-06-30 Date/Publication: 2022-07-03 source.ver: src/contrib/ggcyto_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggcyto_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ggcyto_1.24.1.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R importsMe: CytoML suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace, openCyto dependencyCount: 84 Package: ggmanh Version: 1.0.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), markdown, GenomicRanges License: MIT + file LICENSE MD5sum: 65d0f00ed391070b517452cfbc7772ee NeedsCompilation: no Title: Visualization Tool for GWAS Result Description: Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints. biocViews: Visualization, GenomeWideAssociation, Genetics Author: John Lee [aut, cre], Xiuwen Zheng [ctb, dtc] Maintainer: John Lee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: RELEASE_3_15 git_last_commit: 6fa7bf9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/ggmanh_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggmanh_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggmanh_1.0.0.tgz vignettes: vignettes/ggmanh/inst/doc/ggmanh.html vignetteTitles: Guide to ggmanh Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R suggestsMe: SAIGEgds dependencyCount: 55 Package: ggmsa Version: 1.2.3 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggalt, ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods, statebins, ggtree (>= 1.17.1) Suggests: ggtreeExtra, ape, cowplot, knitr, BiocStyle, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 53c7b9596370a6f85185ccc95fd14f9a NeedsCompilation: no Title: Plot Multiple Sequence Alignment using 'ggplot2' Description: A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. biocViews: Software, Visualization, Alignment, Annotation, MultipleSequenceAlignment Author: Lang Zhou [aut, cre], Guangchuang Yu [aut, ths] (), Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Lang Zhou URL: http://yulab-smu.top/ggmsa/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: RELEASE_3_15 git_last_commit: 781b7aa git_last_commit_date: 2022-05-16 Date/Publication: 2022-05-17 source.ver: src/contrib/ggmsa_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggmsa_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ggmsa_1.2.3.tgz vignettes: vignettes/ggmsa/inst/doc/ggmsa.html vignetteTitles: ggmsa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R dependencyCount: 102 Package: GGPA Version: 1.8.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: 331a7261e04dc74b418b8a6d59df8a45 NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: RELEASE_3_15 git_last_commit: 0214e3d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GGPA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GGPA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GGPA_1.8.0.tgz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 60 Package: ggspavis Version: 1.2.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, methods, stats Suggests: BiocStyle, rmarkdown, knitr, STexampleData, BumpyMatrix, scater, scran, uwot, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 336cf4cc2e377ed61aaa661787a54e78 NeedsCompilation: no Title: Visualization functions for spatially resolved transcriptomics data Description: Visualization functions for spatially resolved transcriptomics datasets stored in SpatialExperiment format. Includes functions to create several types of plots for data from from spot-based (e.g. 10x Genomics Visium) and molecule-based (e.g. seqFISH) technological platforms. biocViews: SingleCell, Transcriptomics, Spatial Author: Lukas M. Weber [aut, cre] (), Helena L. Crowell [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: RELEASE_3_15 git_last_commit: ffb2eb5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ggspavis_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggspavis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggspavis_1.2.0.tgz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R dependencyCount: 110 Package: ggtree Version: 3.4.4 Depends: R (>= 3.5.0) Imports: ape, aplot, dplyr, ggplot2 (>= 3.0.0), grid, magrittr, methods, purrr, rlang, ggfun (>= 0.0.6), yulab.utils, tidyr, tidytree (>= 0.3.9), treeio (>= 1.8.0), utils, scales Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr, prettydoc, rmarkdown, stats, testthat, tibble, glue License: Artistic-2.0 MD5sum: 927ef3aff91ca06064d3d86b56f75d0c NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_15 git_last_commit: 8e48d3e git_last_commit_date: 2022-09-26 Date/Publication: 2022-09-27 source.ver: src/contrib/ggtree_3.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggtree_3.4.4.zip mac.binary.ver: bin/macosx/contrib/4.2/ggtree_3.4.4.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R dependsOnMe: tanggle importsMe: cogeqc, enrichplot, ggmsa, ggtreeExtra, LinTInd, LymphoSeq, miaViz, microbiomeMarker, MicrobiotaProcess, orthogene, philr, singleCellTK, sitePath, systemPipeTools, treekoR, dowser, EvoPhylo, genBaRcode, ggmotif, harrietr, numbat, Platypus, shinyTempSignal, STraTUS, SurvivalPath, Sysrecon suggestsMe: compcodeR, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE, deeptime, ggimage, idiogramFISH, nosoi, oppr, PCMBase, RAINBOWR, rhierbaps, yatah dependencyCount: 57 Package: ggtreeExtra Version: 1.6.1 Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree, tidytree (>= 0.3.9) Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar License: GPL (>= 3) MD5sum: a615b48bfb0d8f0aa103148e9b0c5253 NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: RELEASE_3_15 git_last_commit: b8344b4 git_last_commit_date: 2022-09-18 Date/Publication: 2022-09-20 source.ver: src/contrib/ggtreeExtra_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggtreeExtra_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ggtreeExtra_1.6.1.tgz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R importsMe: MicrobiotaProcess suggestsMe: enrichplot, ggmsa dependencyCount: 59 Package: GIGSEA Version: 1.14.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 1c4ebebbe2d9a8b14143defc35989982 NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: RELEASE_3_15 git_last_commit: f9d095c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GIGSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GIGSEA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GIGSEA_1.14.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: girafe Version: 1.48.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 MD5sum: f7f1df7f4f5b44ff345df89f20692c51 NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/girafe git_branch: RELEASE_3_15 git_last_commit: b0d0e09 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/girafe_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/girafe_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/girafe_1.48.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R dependencyCount: 52 Package: GISPA Version: 1.20.0 Depends: R (>= 3.5) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 Archs: x64 MD5sum: 50b864c7182e5084bf7b5866d914c760 NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GISPA git_branch: RELEASE_3_15 git_last_commit: 6f47f82 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GISPA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GISPA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GISPA_1.20.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R dependencyCount: 135 Package: GLAD Version: 2.60.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: bfe3b731419daeb8f2ff5ebeb2bea517 NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: RELEASE_3_15 git_last_commit: 7d1861d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GLAD_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GLAD_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GLAD_2.60.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads, aroma.cn, aroma.core, cghRA dependencyCount: 4 Package: GladiaTOX Version: 1.12.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 Archs: x64 MD5sum: 3ad017ce262a9d596127acfabcc44a96 NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: RELEASE_3_15 git_last_commit: 8ea056b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GladiaTOX_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GladiaTOX_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GladiaTOX_1.12.0.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 67 Package: Glimma Version: 2.6.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran License: GPL-3 MD5sum: 4eeb7de17a2f89d76a14c79448e30793 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: RELEASE_3_15 git_last_commit: 23220d9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Glimma_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Glimma_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Glimma_2.6.0.tgz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html, vignettes/Glimma/inst/doc/single_cell_edger.html vignetteTitles: DESeq2, Introduction using limma or edgeR, Single Cells with edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R, vignettes/Glimma/inst/doc/single_cell_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools dependencyCount: 99 Package: glmGamPoi Version: 1.8.0 Imports: Rcpp, DelayedMatrixStats, matrixStats, DelayedArray, HDF5Array, SummarizedExperiment, BiocGenerics, methods, stats, utils, splines LinkingTo: Rcpp, RcppArmadillo, beachmat Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, beachmat, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran License: GPL-3 MD5sum: 3a464ce56428f87462f235d63953a55e NeedsCompilation: yes Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: RELEASE_3_15 git_last_commit: b723d61 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/glmGamPoi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/glmGamPoi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/glmGamPoi_1.8.0.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R importsMe: transformGamPoi suggestsMe: DESeq2 dependencyCount: 35 Package: glmSparseNet Version: 1.14.1 Depends: R (>= 4.1), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, biomaRt, futile.logger, futile.options, forcats, utils, dplyr, glue, readr, digest, httr, ggplot2, survminer, reshape2, stringr, parallel, methods Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL-3 Archs: x64 MD5sum: cacac399113f3e1e3298eae87705a0ff NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: RELEASE_3_15 git_last_commit: 0661877 git_last_commit_date: 2022-05-06 Date/Publication: 2022-05-15 source.ver: src/contrib/glmSparseNet_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/glmSparseNet_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/glmSparseNet_1.14.1.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma, Separate 2 groups in Cox regression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R dependencyCount: 174 Package: GlobalAncova Version: 4.14.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: 872b3070df696d94366cf8800a417f21 NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: RELEASE_3_15 git_last_commit: 6f60d8c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GlobalAncova_4.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GlobalAncova_4.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GlobalAncova_4.14.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest dependencyCount: 83 Package: globalSeq Version: 1.24.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 Archs: x64 MD5sum: 943e5b6ab33b6e07885ba9722a2e47c2 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: RELEASE_3_15 git_last_commit: 32ff27e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/globalSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/globalSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/globalSeq_1.24.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.50.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: d3fdd9ddcd80a2507aab0e4ba69b321b NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_15 git_last_commit: 08612a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/globaltest_5.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/globaltest_5.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/globaltest_5.50.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest, SlaPMEG suggestsMe: topGO, penalized dependencyCount: 53 Package: gmapR Version: 1.38.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 35703d18b3691ef4c7f9e47d6f77012f NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: RELEASE_3_15 git_last_commit: f762532 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gmapR_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/gmapR_1.38.0.tgz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: MMAPPR2 suggestsMe: VariantTools, VariantToolsData dependencyCount: 99 Package: GmicR Version: 1.10.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE MD5sum: bd3875a7713dc04476777b5a767d0123 NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: RELEASE_3_15 git_last_commit: e27e644 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GmicR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GmicR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GmicR_1.10.0.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 150 Package: gmoviz Version: 1.8.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, GenomeInfoDb, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager License: GPL-3 MD5sum: 573462644ee1c64bee6b596e32cfde46 NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: RELEASE_3_15 git_last_commit: 0b320c6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gmoviz_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gmoviz_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gmoviz_1.8.0.tgz vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 111 Package: GMRP Version: 1.24.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: ea9a4623ce65d67a9127ec5a05ad4922 NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: RELEASE_3_15 git_last_commit: efcc69c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GMRP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GMRP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GMRP_1.24.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 21 Package: GNET2 Version: 1.12.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: cd4951b9cc4629d68221f08b47b08464 NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: RELEASE_3_15 git_last_commit: f3c81f4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GNET2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GNET2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GNET2_1.12.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 91 Package: GOexpress Version: 1.30.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) Archs: x64 MD5sum: 079f50b85688be624a7ff42a4792466f NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: RELEASE_3_15 git_last_commit: 999b938 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOexpress_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOexpress_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOexpress_1.30.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 93 Package: GOfuncR Version: 1.16.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat License: GPL (>= 2) MD5sum: 638867307323402f62639764f7efaf8b NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (01-May-2021). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: RELEASE_3_15 git_last_commit: 603fc79 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOfuncR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOfuncR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOfuncR_1.16.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R dependencyCount: 53 Package: GOpro Version: 1.22.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 Archs: x64 MD5sum: e76f8955277e9adef15d1322e7c1a2a4 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: RELEASE_3_15 git_last_commit: bb93a04 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOpro_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOpro_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOpro_1.22.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 95 Package: goProfiles Version: 1.58.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 Archs: x64 MD5sum: 9d13568ec704067352082d68db372002 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: RELEASE_3_15 git_last_commit: 2ee94e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/goProfiles_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goProfiles_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goProfiles_1.58.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R dependencyCount: 50 Package: GOSemSim Version: 2.22.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat, ROCR License: Artistic-2.0 MD5sum: d36f9aa6d40d49c0b310ba5dd3876e27 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_15 git_last_commit: fd74aeb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOSemSim_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOSemSim_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOSemSim_2.22.0.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome, BiSEp importsMe: clusterProfiler, DOSE, enrichplot, GAPGOM, meshes, Rcpi, rrvgo, simplifyEnrichment, ViSEAGO suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, rDNAse dependencyCount: 46 Package: goseq Version: 1.48.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) Archs: x64 MD5sum: 77e3e03af4dd255349ee17b0166ac481 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_15 git_last_commit: d077fda git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/goseq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goseq_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goseq_1.48.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, SMITE suggestsMe: sparrow dependencyCount: 103 Package: GOSim Version: 1.34.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) MD5sum: a0f40d2cbc626bedd54fbf31735206db NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich git_url: https://git.bioconductor.org/packages/GOSim git_branch: RELEASE_3_15 git_last_commit: 96763f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOSim_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOSim_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOSim_1.34.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R dependencyCount: 64 Package: goSTAG Version: 1.20.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 41f25a94a26ba19391021bad21296978 NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: RELEASE_3_15 git_last_commit: 07a1cb1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/goSTAG_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goSTAG_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goSTAG_1.20.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 72 Package: GOstats Version: 2.62.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 4437d9cafb849c0c951f20d13bafb425 NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_15 git_last_commit: 217db03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOstats_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOstats_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOstats_2.62.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA, PloGO2 importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, MIGSA, miRLAB, netZooR, pcaExplorer, scTensor, DNLC suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA, maGUI, sand dependencyCount: 62 Package: GOsummaries Version: 2.32.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) MD5sum: d9d4cc9eb60574d23c263ecc8a69fb9f NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries git_url: https://git.bioconductor.org/packages/GOsummaries git_branch: RELEASE_3_15 git_last_commit: b3facb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOsummaries_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOsummaries_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOsummaries_2.32.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R dependencyCount: 46 Package: GOTHiC Version: 1.32.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, GenomeInfoDb Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 Archs: x64 MD5sum: 0afefca0516b7d0a287a7b1a56ec4187 NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: RELEASE_3_15 git_last_commit: ba72ba3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GOTHiC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOTHiC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOTHiC_1.32.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R dependencyCount: 85 Package: goTools Version: 1.70.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 52c73a5d407dbf8dfc07df69af2308ae NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: RELEASE_3_15 git_last_commit: 571c527 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/goTools_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goTools_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goTools_1.70.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 46 Package: GPA Version: 1.8.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: a75757746a82265cad981a329a1b4727 NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: RELEASE_3_15 git_last_commit: dc71d85 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GPA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GPA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GPA_1.8.0.tgz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 72 Package: gpart Version: 1.13.0 Depends: R (>= 3.5.0), grid, Homo.sapiens, TxDb.Hsapiens.UCSC.hg38.knownGene, Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi, grDevices, stats, utils, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: f47c07705350c8528d7608287bbe2185 NeedsCompilation: yes Title: Human genome partitioning of dense sequencing data by identifying haplotype blocks Description: we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package. biocViews: Software, Clustering Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph] Maintainer: Sun Ah Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gpart git_branch: master git_last_commit: e388fd0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/gpart_1.13.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/gpart_1.13.0.tgz vignettes: vignettes/gpart/inst/doc/gpart.html vignetteTitles: Your Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gpart/inst/doc/gpart.R dependencyCount: 108 Package: gpls Version: 1.68.0 Imports: stats Suggests: MASS License: Artistic-2.0 Archs: x64 MD5sum: 988c10f56ff788ab3bc005619df24fd3 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: RELEASE_3_15 git_last_commit: 4307558 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gpls_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gpls_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gpls_1.68.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: gprege Version: 1.39.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 6c08e8b582d278cf158912bcb4b4e5da NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, Bioinformatics, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com git_url: https://git.bioconductor.org/packages/gprege git_branch: master git_last_commit: 570c1d1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gprege_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gprege_1.39.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gprege_1.39.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R dependencyCount: 1 Package: gpuMagic Version: 1.12.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 0c682198360a1480b5129939ab084fd9 NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For Windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For Linux user (Except mac): ocl-icd-opencl-dev package is required. For Mac user, no action is needed for the system has installed the dependency. 4. GNU make VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: RELEASE_3_15 git_last_commit: 48f5df9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gpuMagic_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gpuMagic_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gpuMagic_1.12.0.tgz vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html, vignettes/gpuMagic/inst/doc/Quick_start_guide.html vignetteTitles: Customized_opencl_code, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R, vignettes/gpuMagic/inst/doc/Quick_start_guide.R dependencyCount: 62 Package: GRaNIE Version: 1.0.7 Depends: R (>= 4.2.0), tidyverse, topGO Imports: futile.logger, checkmate, patchwork, reshape2, data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer, ComplexHeatmap, DESeq2, csaw, circlize, robust, progress, utils, methods, stringr, scales, BiocManager, BiocParallel, igraph, S4Vectors, ggplot2, rlang, Biostrings, GenomeInfoDb, IRanges, SummarizedExperiment, forcats, gridExtra, limma, purrr, tidyselect, readr, grid, tidyr, dplyr, stats, grDevices, graphics, magrittr, tibble, viridis, BiocFileCache, colorspace Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Hs.eg.db, org.Mm.eg.db, IHW, biomaRt, clusterProfiler, ReactomePA, DOSE, ChIPseeker, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 2d99608a1a38236b9584f59fddd3b56f NeedsCompilation: no Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNA-seq data Description: Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have celltype specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally TF activity data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach. biocViews: Software, GeneExpression, GeneRegulation, NetworkInference, GeneSetEnrichment, BiomedicalInformatics, Genetics, Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression, Transcription, ChIPSeq Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb] Maintainer: Christian Arnold URL: https://grp-zaugg.embl-community.io/GRaNIE VignetteBuilder: knitr BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues git_url: https://git.bioconductor.org/packages/GRaNIE git_branch: RELEASE_3_15 git_last_commit: 2ff0659 git_last_commit_date: 2022-09-27 Date/Publication: 2022-09-29 source.ver: src/contrib/GRaNIE_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRaNIE_1.0.7.zip mac.binary.ver: bin/macosx/contrib/4.2/GRaNIE_1.0.7.tgz vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html vignetteTitles: Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R dependencyCount: 189 Package: granulator Version: 1.4.0 Depends: R (>= 4.1) Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify, grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap, purrr, rlang, stats, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e6dcca7436730ed11a1f018c43384517 NeedsCompilation: no Title: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data Description: granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well. biocViews: RNASeq, GeneExpression, DifferentialExpression, Transcriptomics, SingleCell, StatisticalMethod, Regression Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico Ferrero [aut] Maintainer: Sabina Pfister URL: https://github.com/xanibas/granulator VignetteBuilder: knitr BugReports: https://github.com/xanibas/granulator/issues git_url: https://git.bioconductor.org/packages/granulator git_branch: RELEASE_3_15 git_last_commit: 13d4752 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/granulator_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/granulator_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/granulator_1.4.0.tgz vignettes: vignettes/granulator/inst/doc/granulator.html vignetteTitles: Deconvoluting bulk RNA-seq data with granulator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/granulator/inst/doc/granulator.R suggestsMe: deconvR dependencyCount: 101 Package: graper Version: 1.12.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 9ddc942127e7e60e4fd91e522019c6c8 NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: RELEASE_3_15 git_last_commit: d7ee5fa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/graper_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/graper_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/graper_1.12.0.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 41 Package: graph Version: 1.74.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: 1f44fcb15454032a94bc9b41228daa80 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_15 git_last_commit: 4af608a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/graph_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/graph_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/graph_1.74.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL, RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, ppiData, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler, hasseDiagram, PairViz, PerfMeas, RSeed, SubpathwayLNCE importsMe: alpine, AnnotationHubData, BgeeDB, BiocCheck, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, BioPlex, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga, flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM, GeneNetworkBuilder, GenomicInteractionNodes, GOSim, GraphAT, graphite, hyperdraw, KEGGgraph, keggorthology, MIGSA, mnem, NCIgraph, NeighborNet, netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi, pathview, PFP, PhenStat, pkgDepTools, ppiStats, pwOmics, qpgraph, RCy3, RGraph2js, RpsiXML, rsbml, Rtreemix, SplicingGraphs, Streamer, trackViewer, VariantFiltering, abn, BayesNetBP, BiDAG, bnClustOmics, BNrich, ceg, CePa, classGraph, CodeDepends, cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase, gridDebug, gRim, HEMDAG, HydeNet, kpcalg, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, rsolr, rSpectral, SEMgraph, simPATHy, stablespec, topologyGSA, unifDAG, wiseR, zenplots suggestsMe: AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct, bsub, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebnUtils, textplot, tidygraph dependencyCount: 6 Package: GraphAlignment Version: 1.60.0 License: file LICENSE License_restricts_use: yes Archs: x64 MD5sum: ff105967228bc7a38bdcb09182c8054e NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: RELEASE_3_15 git_last_commit: 9af9003 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GraphAlignment_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphAlignment_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphAlignment_1.60.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.68.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL Archs: x64 MD5sum: e365e9d118ebf22e47dd23ef84926975 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: RELEASE_3_15 git_last_commit: 118e419 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GraphAT_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphAT_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphAT_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 20 Package: graphite Version: 1.42.0 Depends: R (>= 4.2), methods Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics, rlang Suggests: checkmate, a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: eaf4e8fb63d35acd3f4de43beffacf66 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, Metabolomics Author: Gabriele Sales [cre], Enrica Calura [aut], Chiara Romualdi [aut] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/graphite VignetteBuilder: R.rsp BugReports: https://github.com/sales-lab/graphite/issues git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_15 git_last_commit: 634ec4d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/graphite_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/graphite_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/graphite_1.42.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: PoTRA importsMe: dce, EnrichmentBrowser, mogsa, multiGSEA, ReactomePA, sSNAPPY, StarBioTrek, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping dependencyCount: 47 Package: GraphPAC Version: 1.38.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 47c80d801f91679be2361ff7a6b4127f NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: RELEASE_3_15 git_last_commit: 582d594 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GraphPAC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphPAC_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphPAC_1.38.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC dependencyCount: 40 Package: GRENITS Version: 1.48.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: 90821143ba41931aac8e3223c7eb8332 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: RELEASE_3_15 git_last_commit: be1c0c1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GRENITS_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRENITS_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GRENITS_1.48.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 43 Package: GreyListChIP Version: 1.28.1 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: x64 MD5sum: 142d9f44d8ff06737e98df8f248c5e35 NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Gord Brown Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_15 git_last_commit: 4148644 git_last_commit_date: 2022-05-13 Date/Publication: 2022-05-15 source.ver: src/contrib/GreyListChIP_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GreyListChIP_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GreyListChIP_1.28.1.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: DiffBind, epigraHMM dependencyCount: 47 Package: GRmetrics Version: 1.22.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: bf0cadb2312faa0d09033719cae65126 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_15 git_last_commit: cdeed1e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GRmetrics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRmetrics_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GRmetrics_1.22.0.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 129 Package: groHMM Version: 1.30.1 Depends: R (>= 3.5.0), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 5b2cceceb9359b33fdaa3504338c05ad NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Anusha Nagari , Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: RELEASE_3_15 git_last_commit: efe650e git_last_commit_date: 2022-05-11 Date/Publication: 2022-05-15 source.ver: src/contrib/groHMM_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/groHMM_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/groHMM_1.30.1.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 46 Package: GRridge Version: 1.20.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 MD5sum: 84bba3012a6d424b300dff72602a70b9 NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork, ImmunoOncology Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel git_url: https://git.bioconductor.org/packages/GRridge git_branch: RELEASE_3_15 git_last_commit: 08e1cc9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GRridge_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/GRridge_1.20.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R dependencyCount: 24 Package: GSALightning Version: 1.24.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: e72d9c87aeb1fbd8c21d36e328df4695 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: RELEASE_3_15 git_last_commit: 6a858e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSALightning_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSALightning_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSALightning_1.24.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.30.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: ea9c8bc889cd394677baf8c2719050a4 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: RELEASE_3_15 git_last_commit: dd85c8f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSAR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSAR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSAR_1.30.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 12 Package: GSCA Version: 2.26.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 0e6d0b6cb54adfa671815faf84c573ed NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: RELEASE_3_15 git_last_commit: 146187f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSCA_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSCA_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSCA_2.26.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 73 Package: gscreend Version: 1.10.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: eb512657be36e6af06b4f25bbdc84695 NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: RELEASE_3_15 git_last_commit: 273e2b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gscreend_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gscreend_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gscreend_1.10.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 75 Package: GSEABase Version: 1.58.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr License: Artistic-2.0 MD5sum: aa2f7bdcb38caca0a62d6d1d2f574187 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_15 git_last_commit: 7de0444 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSEABase_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEABase_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEABase_1.58.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, cosmosR, EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, PanomiR, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, sparrow, TFutils, vissE, msigdb, SingscoreAMLMutations, clustermole, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, BaseSet dependencyCount: 49 Package: GSEABenchmarkeR Version: 1.16.0 Depends: R (>= 3.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown License: Artistic-2.0 MD5sum: cc7717c4504fc899be45a71de1823d90 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: RELEASE_3_15 git_last_commit: 657ddbb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSEABenchmarkeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEABenchmarkeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEABenchmarkeR_1.16.0.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R dependencyCount: 124 Package: GSEAlm Version: 1.56.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 17c4c54e625a7f04ae6780d0197cb156 NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: RELEASE_3_15 git_last_commit: d910956 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSEAlm_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEAlm_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEAlm_1.56.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 6 Package: GSEAmining Version: 1.6.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat License: GPL-3 | file LICENSE MD5sum: 2a982f5107096b38808f2f081d809948 NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: RELEASE_3_15 git_last_commit: 9a58d1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSEAmining_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEAmining_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEAmining_1.6.0.tgz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 55 Package: gsean Version: 1.16.1 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: e247eba9844d58382722d6714b576316 NeedsCompilation: yes Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: RELEASE_3_15 git_last_commit: 1a97fd7 git_last_commit_date: 2022-08-17 Date/Publication: 2022-08-18 source.ver: src/contrib/gsean_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/gsean_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/gsean_1.16.1.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 118 Package: GSgalgoR Version: 1.6.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 7e7501db2273ef4df144bbdb2a489c20 NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: RELEASE_3_15 git_last_commit: 3d74742 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSgalgoR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSgalgoR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSgalgoR_1.6.0.tgz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.30.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: 03225a9bed56c250980d75739d1c7ce5 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: RELEASE_3_15 git_last_commit: 69afe4a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSReg_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSReg_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSReg_1.30.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 105 Package: GSRI Version: 2.44.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 Archs: x64 MD5sum: a2639ef2451a38f129f7077a92432bfc NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: RELEASE_3_15 git_last_commit: 9e0589f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GSRI_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSRI_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSRI_2.44.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 64 Package: GSVA Version: 1.44.5 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel, BiocParallel, SingleCellExperiment, sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array, BiocSingular Suggests: BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs License: GPL (>= 2) MD5sum: 68e460bb2c74873b9f7b5d19a8444a06 NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways, GeneSetEnrichment Author: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GSVA VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: RELEASE_3_15 git_last_commit: bd55a3a git_last_commit_date: 2022-09-20 Date/Publication: 2022-09-22 source.ver: src/contrib/GSVA_1.44.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSVA_1.44.5.zip mac.binary.ver: bin/macosx/contrib/4.2/GSVA_1.44.5.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.html vignetteTitles: Gene set variation analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: MM2S importsMe: consensusOV, EGSEA, escape, oppar, singleCellTK, TBSignatureProfiler, TNBC.CMS, clustermole, DRviaSPCN, psSubpathway, scMappR, SIGN, SMDIC suggestsMe: decoupleR, MCbiclust, sparrow dependencyCount: 79 Package: gtrellis Version: 1.28.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE Archs: x64 MD5sum: 58ad6af0171e0cba751c209f86b9f27a NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_15 git_last_commit: d770a7b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gtrellis_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gtrellis_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gtrellis_1.28.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 25 Package: GUIDEseq Version: 1.26.0 Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 486c7024771ed0c5212376b14bb7be86 NeedsCompilation: no Title: GUIDE-seq and PEtag-seq analysis pipeline Description: The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: RELEASE_3_15 git_last_commit: 46778e1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GUIDEseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GUIDEseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GUIDEseq_1.26.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R importsMe: crisprseekplus dependencyCount: 153 Package: Guitar Version: 2.12.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: d47057f5edf5baac9712570273025232 NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: RELEASE_3_15 git_last_commit: 864335b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Guitar_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Guitar_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Guitar_2.12.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 115 Package: Gviz Version: 1.40.1 Depends: R (>= 4.2), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb (>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: ca89b93cfa1f9aa81945001e6f0a5b22 NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb], Mike Smith [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: RELEASE_3_15 git_last_commit: d218437 git_last_commit_date: 2022-05-03 Date/Publication: 2022-05-03 source.ver: src/contrib/Gviz_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Gviz_1.40.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Gviz_1.40.1.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, coMET, cummeRbund, DMRforPairs, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpediaFI, ASpli, CAGEfightR, comapr, DMRcate, ELMER, extraChIPs, GenomicInteractions, maser, mCSEA, MEAL, methylPipe, motifbreakR, OGRE, PING, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, epimutacions, fishpond, GenomicRanges, gwascat, interactiveDisplay, InterMineR, Pi, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 145 Package: GWAS.BAYES Version: 1.6.0 Depends: R (>= 4.0), Rcpp (>= 1.0.3), RcppEigen (>= 0.3.3.7.0), GA (>= 3.2), caret (>= 6.0-86), ggplot2 (>= 3.3.0), doParallel (>= 1.0.15), memoise (>= 1.1.0), reshape2 (>= 1.4.4), Matrix (>= 1.2-18) LinkingTo: RcppEigen (>= 0.3.3.7.0),Rcpp (>= 1.0.3) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, qqman License: GPL-2 | GPL-3 MD5sum: 1f1b11f352c8f877aa28709b51cdd050 NeedsCompilation: yes Title: GWAS for Selfing Species Description: This package is built to perform GWAS analysis for selfing species. The research related to this package was supported in part by National Science Foundation Award 1853549. biocViews: AssayDomain, SNP Author: Jake Williams [aut, cre], Marco Ferreira [aut], Tieming Ji [aut] Maintainer: Jake Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: RELEASE_3_15 git_last_commit: d3dab45 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GWAS.BAYES_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWAS.BAYES_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GWAS.BAYES_1.6.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.html vignetteTitles: GWAS.BAYES hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.R dependencyCount: 89 Package: gwascat Version: 2.28.1 Depends: R (>= 3.5.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, Gviz, Rsamtools, IRanges, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 Archs: x64 MD5sum: d5fbbd552874f4f835b6126ee04d7629 NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: RELEASE_3_15 git_last_commit: 10f9469 git_last_commit_date: 2022-05-06 Date/Publication: 2022-05-15 source.ver: src/contrib/gwascat_2.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/gwascat_2.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/gwascat_2.28.1.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 129 Package: GWASTools Version: 1.42.1 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel License: Artistic-2.0 MD5sum: 8739e8217e29906b6b58eb07cd6ab24a NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson, David Levine Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_15 git_last_commit: d74527c git_last_commit_date: 2022-08-22 Date/Publication: 2022-08-23 source.ver: src/contrib/GWASTools_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWASTools_1.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GWASTools_1.42.1.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata importsMe: GBScleanR, GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 76 Package: gwasurvivr Version: 1.14.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7b0772e0ad221d5697d678deb341fc56 NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: RELEASE_3_15 git_last_commit: 221dd45 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/gwasurvivr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gwasurvivr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gwasurvivr_1.14.0.tgz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 135 Package: GWENA Version: 1.6.0 Depends: R (>= 4.1) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>= 4.0.4), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: ccb87948ae4e68b97055331a8dedb4aa NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] (), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: RELEASE_3_15 git_last_commit: 08473e2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GWENA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWENA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GWENA_1.6.0.tgz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 137 Package: h5vc Version: 2.30.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.99.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.15.3) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: 95cd8210a247714cf232ec2bd7375025 NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: RELEASE_3_15 git_last_commit: 40159b9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/h5vc_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/h5vc_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/h5vc_2.30.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 88 Package: hapFabia Version: 1.38.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: x64 MD5sum: 17d9f3743a25b31b1bf09182d9522dc0 NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: RELEASE_3_15 git_last_commit: 2ee5996 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hapFabia_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hapFabia_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hapFabia_1.38.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 8 Package: Harman Version: 1.24.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel, methods, matrixStats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: x64 MD5sum: bb4ed628e1f25effe55117208594aedb NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_15 git_last_commit: ce6b2ab git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Harman_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Harman_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Harman_1.24.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser suggestsMe: HarmanData dependencyCount: 11 Package: Harshlight Version: 1.68.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: d85c5538efab3ed261b70db921e9a515 NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: RELEASE_3_15 git_last_commit: c2a1fd7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Harshlight_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Harshlight_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Harshlight_1.68.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R dependencyCount: 27 Package: hca Version: 1.4.3 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache, tools, utils, digest Suggests: futile.logger, LoomExperiment, SummarizedExperiment, SingleCellExperiment, S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: ee6d30425e8ccc18e6eb7895e6180cae NeedsCompilation: no Title: Exploring the Human Cell Atlas Data Coordinating Platform Description: This package provides users with the ability to query the Human Cell Atlas data repository for single-cell experiment data. The `projects()`, `files()`, `samples()` and `bundles()` functions retrieve summary information on each of these indexes; corresponding `*_details()` are available for individual entries of each index. File-based resources can be downloaded using `files_download()`. Advanced use of the package allows the user to page through large result sets, and to flexibly query the 'list-of-lists' structure representing query responses. biocViews: Software, SingleCell Author: Maya McDaniel [aut], Martin Morgan [aut, cre] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: RELEASE_3_15 git_last_commit: 3995bfc git_last_commit_date: 2022-07-21 Date/Publication: 2022-07-21 source.ver: src/contrib/hca_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/hca_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/hca_1.4.3.tgz vignettes: vignettes/hca/inst/doc/hca_manifest_vignette.html, vignettes/hca/inst/doc/hca_vignette.html vignetteTitles: Working With Human Cell Atlas Manifests, Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hca/inst/doc/hca_manifest_vignette.R, vignettes/hca/inst/doc/hca_vignette.R dependencyCount: 57 Package: HDF5Array Version: 1.24.2 Depends: R (>= 3.4), methods, DelayedArray (>= 0.15.16), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>= 0.31.5), S4Vectors, IRanges LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, RUnit, SingleCellExperiment License: Artistic-2.0 MD5sum: 5f12611e6ffe920c6b45a7f885f043e3 NeedsCompilation: yes Title: HDF5 backend for DelayedArray objects Description: Implement the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array SystemRequirements: GNU make BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_15 git_last_commit: fb213ba git_last_commit_date: 2022-08-01 Date/Publication: 2022-08-02 source.ver: src/contrib/HDF5Array_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/HDF5Array_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/HDF5Array_1.24.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: compartmap, MAGAR, restfulSEData, TENxBrainData, TENxPBMCData importsMe: biscuiteer, bsseq, Cepo, clusterExperiment, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, LoomExperiment, methrix, minfi, MOFA2, netSmooth, NxtIRFcore, recountmethylation, scmeth, scry, signatureSearch, spatialHeatmap, transformGamPoi, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, curatedTCGAData, HCAData, imcdatasets, MethylSeqData, SingleCellMultiModal, TabulaMurisSenisData suggestsMe: beachmat, BiocSklearn, cellxgenedp, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MuData, MultiAssayExperiment, PDATK, QFeatures, SCArray, scMerge, scran, SummarizedExperiment, zellkonverter, digitalDLSorteR dependencyCount: 19 Package: HDTD Version: 1.30.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 364da515bf44cb8ff8e367b1395ba5f4 NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (), John C. Marioni [aut] (), Simon Tavar\'{e} [aut] () Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: RELEASE_3_15 git_last_commit: 853eb58 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HDTD_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HDTD_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HDTD_1.30.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: heatmaps Version: 1.20.0 Depends: R (>= 3.5.0) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 7d609a8f26eeda54a104c1862c348855 NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: RELEASE_3_15 git_last_commit: ce1d974 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/heatmaps_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/heatmaps_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/heatmaps_1.20.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 40 Package: Heatplus Version: 3.4.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 69001410ff539c03d6a153cc0f89cdb0 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: RELEASE_3_15 git_last_commit: acbc000 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Heatplus_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Heatplus_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Heatplus_3.4.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics dependencyCount: 4 Package: HelloRanges Version: 1.22.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment, BiocIO Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: 1330294814dfa8a0ce9745bd1c466feb NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: RELEASE_3_15 git_last_commit: 7a1e113 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HelloRanges_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HelloRanges_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HelloRanges_1.22.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 100 Package: HELP Version: 1.54.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 489528ae7591518572889777028521de NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: RELEASE_3_15 git_last_commit: c83c988 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HELP_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HELP_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HELP_1.54.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 7 Package: HEM Version: 1.68.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: 857959fe14207967446363176f870d52 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: RELEASE_3_15 git_last_commit: 5ebd762 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HEM_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HEM_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HEM_1.68.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: hermes Version: 1.0.1 Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16) Imports: assertthat, biomaRt, Biobase, BiocGenerics, checkmate (>= 2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats, forcats, GenomicRanges, ggplot2, ggrepel (>= 0.9), IRanges, lifecycle, limma, magrittr, matrixStats, methods, MultiAssayExperiment, purrr, R6, Rdpack, rlang, stats, S4Vectors, tidyr, utils Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown, statmod, testthat (>= 2.0), vdiffr, withr License: Apache License 2.0 | file LICENSE Archs: x64 MD5sum: 2d23ba47ab6666845be9c22186a91ef3 NeedsCompilation: no Title: Preprocessing, analyzing, and reporting of RNA-seq data Description: Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed RNA-seq data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from BioMart. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including `cpm`, `rpkm` and `tpm` can be used, and `DESeq2` as well as `voom` differential expression analyses are available. biocViews: RNASeq, DifferentialExpression, Normalization, Preprocessing, QualityControl Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut] Maintainer: Daniel Sabanés Bové URL: https://github.com/insightsengineering/hermes/ VignetteBuilder: knitr BugReports: https://github.com/insightsengineering/hermes/issues git_url: https://git.bioconductor.org/packages/hermes git_branch: RELEASE_3_15 git_last_commit: 0e43e46 git_last_commit_date: 2022-05-05 Date/Publication: 2022-05-15 source.ver: src/contrib/hermes_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/hermes_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/hermes_1.0.1.tgz vignettes: vignettes/hermes/inst/doc/introduction.html vignetteTitles: Introduction to `hermes` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hermes/inst/doc/introduction.R dependencyCount: 136 Package: HGC Version: 1.4.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 2a7013e925918c7e744df1230774050e NeedsCompilation: yes Title: A fast hierarchical graph-based clustering method Description: HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer: XGlab SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: RELEASE_3_15 git_last_commit: 5e2a363 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HGC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HGC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HGC_1.4.0.tgz vignettes: vignettes/HGC/inst/doc/HGC.html vignetteTitles: HGC package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HGC/inst/doc/HGC.R dependencyCount: 51 Package: hiAnnotator Version: 1.30.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) MD5sum: 18b50b9a829bc73fc56cf19fa93883ba NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: RELEASE_3_15 git_last_commit: d94fe90 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hiAnnotator_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hiAnnotator_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hiAnnotator_1.30.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor dependencyCount: 79 Package: HIBAG Version: 1.32.0 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown License: GPL-3 Archs: x64 MD5sum: 4007a241b0e6d53a2a0be548fce9977f NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (), Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: RELEASE_3_15 git_last_commit: fc2997f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HIBAG_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIBAG_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HIBAG_1.32.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HiCBricks Version: 1.14.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, GenomeInfoDb, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 363fda03dffe5428d2220d0bd273638a NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: RELEASE_3_15 git_last_commit: 5a6994b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HiCBricks_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCBricks_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCBricks_1.14.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R dependencyCount: 86 Package: HiCcompare Version: 1.18.0 Depends: R (>= 3.5.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE Archs: x64 MD5sum: cdaa727ff789860d9f50bd7dd06dd433 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Kellen Cresswell , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: RELEASE_3_15 git_last_commit: 27235fc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HiCcompare_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCcompare_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCcompare_1.18.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, SpectralTAD, TADCompare dependencyCount: 95 Package: HiCDCPlus Version: 1.4.2 Imports: Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors LinkingTo: Rcpp Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix, BiocFileCache, rappdirs Enhances: parallel License: GPL-3 MD5sum: 83323139af0ac911fad9a8abc1b3918f NeedsCompilation: yes Title: Hi-C Direct Caller Plus Description: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation. biocViews: HiC, DNA3DStructure, Software, Normalization Author: Merve Sahin [cre, aut] () Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: RELEASE_3_15 git_last_commit: 70d7860 git_last_commit_date: 2022-06-06 Date/Publication: 2022-06-07 source.ver: src/contrib/HiCDCPlus_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCDCPlus_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCDCPlus_1.4.2.tgz vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R dependencyCount: 160 Package: hierGWAS Version: 1.26.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 7c6259d3c6dd1361be3f382ce3e90bd8 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: RELEASE_3_15 git_last_commit: ab309ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hierGWAS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hierGWAS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hierGWAS_1.26.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 19 Package: hierinf Version: 1.14.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE Archs: x64 MD5sum: e3cd43e425b9516be8d71018d6dfa5f8 NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: RELEASE_3_15 git_last_commit: 6387e96 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hierinf_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hierinf_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hierinf_1.14.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 19 Package: HilbertCurve Version: 1.26.0 Depends: R (>= 3.6.0), grid Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE MD5sum: 5f414ddec854604750421f88d3acd491 NeedsCompilation: no Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_15 git_last_commit: 0b7de6e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HilbertCurve_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HilbertCurve_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HilbertCurve_1.26.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 27 Package: HilbertVis Version: 1.54.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: aea37b928321595863e0b6f748d1b3ea NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: RELEASE_3_15 git_last_commit: a901481 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HilbertVis_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HilbertVis_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HilbertVis_1.54.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve dependencyCount: 6 Package: HilbertVisGUI Version: 1.54.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 15f6b0b84b6ed655c1a58f0d3a19e681 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: RELEASE_3_15 git_last_commit: 0666234 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HilbertVisGUI_1.54.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.10.0 Depends: R(>= 4.1), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: ae9052796af24bdae780f75ffaaf7219 NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.0.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_15 git_last_commit: 3956244 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HiLDA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiLDA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiLDA_1.10.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 124 Package: hipathia Version: 2.12.0 Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 55623954348cfc32eaa8e3fc0c9b9fea NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: RELEASE_3_15 git_last_commit: db2d24b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hipathia_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hipathia_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hipathia_2.12.0.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 115 Package: HIPPO Version: 1.8.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 6f8e82917b102b33ecd5f370c5f2eee4 NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: RELEASE_3_15 git_last_commit: 5eb3f20 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HIPPO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIPPO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HIPPO_1.8.0.tgz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 79 Package: hiReadsProcessor Version: 1.32.0 Depends: R (>= 3.5.0), Biostrings, GenomicAlignments, BiocParallel, hiAnnotator Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat, markdown License: GPL-3 MD5sum: cab30ea15fc9ec86f161c80d3f31f449 NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiReadsProcessor git_branch: RELEASE_3_15 git_last_commit: 2107a54 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hiReadsProcessor_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/hiReadsProcessor_1.32.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R dependencyCount: 89 Package: HIREewas Version: 1.14.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: 4a8a2ac11e139c35f3f2f36d6817e388 NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: RELEASE_3_15 git_last_commit: 33b21dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HIREewas_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIREewas_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HIREewas_1.14.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiTC Version: 1.40.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 MD5sum: 7749c33acad2afb92e710c5f67b0806c NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: RELEASE_3_15 git_last_commit: 35a87db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HiTC_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiTC_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiTC_1.40.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust dependencyCount: 46 Package: hmdbQuery Version: 1.16.0 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 5adb371e56d878f4b75bd5d152d72f33 NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: RELEASE_3_15 git_last_commit: ece7e40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hmdbQuery_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hmdbQuery_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hmdbQuery_1.16.0.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 8 Package: HMMcopy Version: 1.38.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: 2fe67e1d1f0ff3ac650d868ff6dd2092 NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_15 git_last_commit: 059bce0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HMMcopy_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HMMcopy_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HMMcopy_1.38.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: hopach Version: 2.56.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 94a59c2f8c50902abcab7001a27de798 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: RELEASE_3_15 git_last_commit: 2ec52f6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hopach_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hopach_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hopach_2.56.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess, seqArchR dependencyCount: 8 Package: HPAanalyze Version: 1.14.0 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, markdown, devtools, BiocStyle License: GPL-3 + file LICENSE Archs: x64 MD5sum: c1283c67b9955533343f02f435f52cae NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran URL: https://github.com/anhtr/HPAanalyze VignetteBuilder: knitr BugReports: https://github.com/anhtr/HPAanalyze/issues git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: RELEASE_3_15 git_last_commit: baeeb43 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HPAanalyze_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPAanalyze_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HPAanalyze_1.14.0.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function with HPAanalyze", "2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human Protein Atlas (HPA) queries", "4. Tutorial: Working with Human Protein Atlas (HPA) xml files offline", "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 46 Package: hpar Version: 1.38.0 Depends: R (>= 3.5.0) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: ca8f44144fda980cbb8c042c7adac8d6 NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto [cre, aut] (), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_15 git_last_commit: 7be158b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hpar_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hpar_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hpar_1.38.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: HPAStainR, pRoloc, RforProteomics dependencyCount: 1 Package: HPAStainR Version: 1.6.0 Depends: R (>= 4.1.0), dplyr, tidyr Imports: utils, stats, scales, stringr, tibble, shiny, data.table Suggests: knitr, BiocManager, qpdf, hpar, testthat, rmarkdown License: Artistic-2.0 MD5sum: dae7d841fbb230d8d0e76b8bb8862527 NeedsCompilation: no Title: Queries the Human Protein Atlas Staining Data for Multiple Proteins and Genes Description: This package is built around the HPAStainR function. The purpose of the HPAStainR function is to query the visual staining data in the Human Protein Atlas to return a table of staining ranked cell types. The function also has multiple arguments to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. The other functions exist exclusively to easily acquire the data required to run HPAStainR. biocViews: GeneExpression, GeneSetEnrichment Author: Tim O. Nieuwenhuis [aut, cre] () Maintainer: Tim O. Nieuwenhuis SystemRequirements: 4GB of RAM VignetteBuilder: knitr BugReports: https://github.com/tnieuwe/HPAstainR git_url: https://git.bioconductor.org/packages/HPAStainR git_branch: RELEASE_3_15 git_last_commit: 7d68438 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HPAStainR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPAStainR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HPAStainR_1.6.0.tgz vignettes: vignettes/HPAStainR/inst/doc/HPAStainR.html vignetteTitles: HPAStainR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HPAStainR/inst/doc/HPAStainR.R dependencyCount: 59 Package: HPiP Version: 1.2.0 Depends: R (>= 4.1) Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE MD5sum: 6b820bd7a65d8f4cf9cb750fe9b00880 NeedsCompilation: no Title: Host-Pathogen Interaction Prediction Description: HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations. biocViews: Proteomics, SystemsBiology, NetworkInference, StructuralPrediction, GenePrediction, Network Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/HPiP VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/HPiP/issues git_url: https://git.bioconductor.org/packages/HPiP git_branch: RELEASE_3_15 git_last_commit: 06d853c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HPiP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPiP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HPiP_1.2.0.tgz vignettes: vignettes/HPiP/inst/doc/HPiP_tutorial.html vignetteTitles: Introduction to HPiP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPiP/inst/doc/HPiP_tutorial.R dependencyCount: 104 Package: HTqPCR Version: 1.50.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 68884e3517042bb368d8af170cb31971 NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_15 git_last_commit: ed0d362 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HTqPCR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HTqPCR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HTqPCR_1.50.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR dependencyCount: 20 Package: HTSeqGenie Version: 4.25.1 Depends: R (>= 3.5.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: 6f52eeb7537801c88965795d353983b7 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder git_url: https://git.bioconductor.org/packages/HTSeqGenie git_branch: master git_last_commit: c66a4ad git_last_commit_date: 2021-11-21 Date/Publication: 2021-11-21 source.ver: src/contrib/HTSeqGenie_4.25.1.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R dependencyCount: 112 Package: HTSFilter Version: 1.36.0 Depends: R (>= 4.0.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 05eff9f6e2f3f37fa5ec2c3c097af389 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: RELEASE_3_15 git_last_commit: ccc98a5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HTSFilter_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HTSFilter_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HTSFilter_1.36.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster dependencyCount: 95 Package: HubPub Version: 1.4.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: 0f71fd02917475790638fecc7704687d NeedsCompilation: no Title: Utilities to create and use Bioconductor Hubs Description: HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket. biocViews: DataImport, Infrastructure, Software, ThirdPartyClient Author: Kayla Interdonato [aut, cre], Martin Morgan [aut] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: RELEASE_3_15 git_last_commit: 10d8f34 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HubPub_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HubPub_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HubPub_1.4.0.tgz vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html, vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub, HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R, vignettes/HubPub/inst/doc/HubPub.R suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub, ExperimentHubData dependencyCount: 78 Package: HumanTranscriptomeCompendium Version: 1.12.0 Depends: R (>= 3.6) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat, rhdf5client, rmarkdown License: Artistic-2.0 MD5sum: 72b96e8c6856caae25e2e61a2596dd8d NeedsCompilation: no Title: Tools to work with a Compendium of 181000 human transcriptome sequencing studies Description: Provide tools for working with a compendium of human transcriptome sequences (originally htxcomp). biocViews: Transcriptomics, Infrastructure Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_15 git_last_commit: 0345c3d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HumanTranscriptomeCompendium_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HumanTranscriptomeCompendium_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HumanTranscriptomeCompendium_1.12.0.tgz vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident collection of sequenced human transcriptomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R dependencyCount: 62 Package: hummingbird Version: 1.6.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: f15fd45e1b1fc411a4d1180a5ebc4254 NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: RELEASE_3_15 git_last_commit: d624b44 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hummingbird_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hummingbird_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hummingbird_1.6.0.tgz vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 26 Package: HybridMTest Version: 1.40.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: fce948481ab9b773e3cc220f02ca8e35 NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: RELEASE_3_15 git_last_commit: ea7d2a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/HybridMTest_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HybridMTest_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HybridMTest_1.40.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R importsMe: APAlyzer dependencyCount: 14 Package: hypeR Version: 1.12.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 636fcf443415a9141e883f55f524f22c NeedsCompilation: no Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: RELEASE_3_15 git_last_commit: be72ad0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hypeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hypeR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hypeR_1.12.0.tgz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 105 Package: hyperdraw Version: 1.48.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: d8b08a43dd2d70ac543e1b7b11ddb05c NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: RELEASE_3_15 git_last_commit: 4428163 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hyperdraw_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hyperdraw_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hyperdraw_1.48.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR dependencyCount: 11 Package: hypergraph Version: 1.68.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 89598a1aa80027a25db8f7e0c1fbf226 NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: RELEASE_3_15 git_last_commit: 7d53b50 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/hypergraph_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hypergraph_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hypergraph_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw, RpsiXML dependencyCount: 7 Package: iASeq Version: 1.40.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 5bc59793e2bd03055e9904348825a9a5 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: RELEASE_3_15 git_last_commit: 8c3702a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iASeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iASeq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iASeq_1.40.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.14.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 08c8b2cede5aa9752855a7dd7fa473e0 NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: RELEASE_3_15 git_last_commit: 9e94f4d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iasva_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iasva_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iasva_1.14.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 36 Package: iBBiG Version: 1.40.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: 27cc05f87f9146e406be76e243de7027 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: RELEASE_3_15 git_last_commit: eef8030 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iBBiG_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iBBiG_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iBBiG_1.40.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 54 Package: ibh Version: 1.44.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: d0c382d9f8bff0582ab2850bd4cc2c31 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: RELEASE_3_15 git_last_commit: 2b8baf6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ibh_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ibh_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ibh_1.44.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.36.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: x64 MD5sum: 0985aa1f21d6b88bdc6ba6c54f0b45bf NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: RELEASE_3_15 git_last_commit: cb27a67 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iBMQ_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iBMQ_1.36.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 38 Package: iCARE Version: 1.24.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE MD5sum: 64b8f867ca5e9641dacaf7e7c9715297 NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/iCARE git_branch: RELEASE_3_15 git_last_commit: 8826f24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iCARE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCARE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCARE_1.24.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 72 Package: Icens Version: 1.68.0 Depends: survival Imports: graphics License: Artistic-2.0 Archs: x64 MD5sum: 0188f3cd5a27a6939e7a5e115aeb4319 NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: RELEASE_3_15 git_last_commit: c2f9723 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Icens_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Icens_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Icens_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.14.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE Archs: x64 MD5sum: e5a07430f8a37f87ebfb6255273089b9 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: RELEASE_3_15 git_last_commit: 5220193 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/icetea_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/icetea_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/icetea_1.14.0.tgz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 133 Package: iCheck Version: 1.26.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: cf4f652ff178d7c458787a25e3738bb0 NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: RELEASE_3_15 git_last_commit: 5e95082 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iCheck_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCheck_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCheck_1.26.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 179 Package: iChip Version: 1.50.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: 4f2bd5ac0cd751ebab47d3604c9777b4 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: RELEASE_3_15 git_last_commit: c555d7b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iChip_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iChip_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iChip_1.50.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 6 Package: iClusterPlus Version: 1.32.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 5fdddec0bd12f04898fc873f18542117 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: RELEASE_3_15 git_last_commit: e8f9cf5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iClusterPlus_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iClusterPlus_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iClusterPlus_1.32.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.16.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 Archs: x64 MD5sum: 14d65b8f9de35960e501f048f5a13d85 NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: RELEASE_3_15 git_last_commit: 1f15fca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iCNV_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCNV_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCNV_1.16.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 91 Package: iCOBRA Version: 1.24.1 Depends: R (>= 4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR, markdown Suggests: knitr, rmarkdown, testthat License: GPL (>=2) Archs: x64 MD5sum: ed73791b9c1773d9ee6235bdc43108de NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results. biocViews: Classification, Visualization Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/iCOBRA VignetteBuilder: knitr BugReports: https://github.com/csoneson/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: RELEASE_3_15 git_last_commit: 133e845 git_last_commit_date: 2022-05-02 Date/Publication: 2022-05-02 source.ver: src/contrib/iCOBRA_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCOBRA_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/iCOBRA_1.24.1.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat, SummarizedBenchmark dependencyCount: 85 Package: ideal Version: 1.20.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, base64enc, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE MD5sum: abd85651f119b240872209c49966afb6 NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_15 git_last_commit: 564dff4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ideal_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ideal_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ideal_1.20.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 205 Package: IdeoViz Version: 1.32.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 MD5sum: c09b62f5df497ced5e918048ee4fa02a NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: RELEASE_3_15 git_last_commit: 4d9ebcd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IdeoViz_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IdeoViz_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IdeoViz_1.32.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R dependencyCount: 46 Package: idiogram Version: 1.72.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 90ee954fe603d70cf7623e4152854317 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: RELEASE_3_15 git_last_commit: 123f7ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/idiogram_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idiogram_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idiogram_1.72.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 49 Package: idpr Version: 1.6.0 Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, msa, ape, testthat, seqinr License: LGPL-3 MD5sum: 976e1414b1c815e5c2c8c434a8f1bd0c NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: RELEASE_3_15 git_last_commit: b486d9a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/idpr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idpr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idpr_1.6.0.tgz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 55 Package: idr2d Version: 1.10.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 204ed2f920a2c55cb6a1cce915a63897 NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (), David Gifford [ths, cph] () Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: RELEASE_3_15 git_last_commit: f92649a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/idr2d_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idr2d_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idr2d_1.10.0.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 66 Package: iGC Version: 1.26.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 463cd6bff5ce7a68558cd43016db9727 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: RELEASE_3_15 git_last_commit: a5a527e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iGC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iGC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iGC_1.26.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.10.0 Depends: methods, R (>= 3.6.0), Rcpp (>= 0.12.0), SummarizedExperiment, StanHeaders (> 2.18.1) Imports: rstan (>= 2.19.2), reshape2 (>= 1.4.3) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, gridExtra, ggrepel License: file LICENSE MD5sum: 39e7a1808695c6989acb609512768445 NeedsCompilation: no Title: Differential gene usage in immune repertoires Description: Detection of biases in immunoglobulin (Ig) gene usage between adaptive immune repertoires that belong to different biological conditions is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage using a probabilistic model which is analyzed computationally by Bayes inference. biocViews: DifferentialExpression, Regression, Genetics, Bayesian Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_15 git_last_commit: 27e0dcc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IgGeneUsage_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/IgGeneUsage_1.10.0.tgz vignettes: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.R dependencyCount: 77 Package: igvR Version: 1.16.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.17.1) Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo, rtracklayer, VariantAnnotation, RColorBrewer Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 3bbe9291ff6de3d2e3badbf491805b47 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_15 git_last_commit: 9b7541b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/igvR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/igvR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/igvR_1.16.0.tgz vignettes: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.html, vignettes/igvR/inst/doc/basicIntro.html, vignettes/igvR/inst/doc/chooseStockOrCustomGenome.html, vignettes/igvR/inst/doc/ctcfChipSeq.html vignetteTitles: "Explore VCF variants,, GWAS snps,, promoters and histone marks around the MEF2C gene in Alzheimers Disease", "Introduction: a simple demo", "Choose a Stock or Custom Genome", "Explore ChIP-seq alignments from a bam file,, MACS2 narrowPeaks,, conservation,, H3K4me3 methylation and motif matching" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.R, vignettes/igvR/inst/doc/basicIntro.R, vignettes/igvR/inst/doc/chooseStockOrCustomGenome.R, vignettes/igvR/inst/doc/ctcfChipSeq.R dependencyCount: 108 Package: IHW Version: 1.24.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: 70e22d11345d213f3c768b340029e471 NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: RELEASE_3_15 git_last_commit: f137f5c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IHW_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IHW_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IHW_1.24.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, SummarizedBenchmark, BloodCancerMultiOmics2017, BisRNA, DGEobj.utils dependencyCount: 9 Package: illuminaio Version: 0.38.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: afd73630ba651ace394ee91fb90e68e0 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: RELEASE_3_15 git_last_commit: e375670 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/illuminaio_0.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/illuminaio_0.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/illuminaio_0.38.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: beadarray, bigmelon, crlmm, methylumi, minfi, sesame suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.6.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d1ca5bb7bfd7f9058ec9040e9591a775 NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: RELEASE_3_15 git_last_commit: 6a4dc41 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ILoReg_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ILoReg_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ILoReg_1.6.0.tgz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 125 Package: imageHTS Version: 1.45.1 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: d4a5e09a0203c999b67965e375a3141e NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry git_url: https://git.bioconductor.org/packages/imageHTS git_branch: master git_last_commit: 264cfbf git_last_commit_date: 2022-01-06 Date/Publication: 2022-01-06 source.ver: src/contrib/imageHTS_1.45.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/imageHTS_1.45.1.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependencyCount: 102 Package: IMAS Version: 1.20.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 Archs: x64 MD5sum: 43980bb8fbe00b60213fded56d4dcd4b NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: RELEASE_3_15 git_last_commit: 9121197 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IMAS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMAS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMAS_1.20.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R dependencyCount: 139 Package: imcRtools Version: 1.2.3 Depends: R (>= 4.1), SpatialExperiment Imports: S4Vectors, stats, utils, SummarizedExperiment, methods, pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind, BiocParallel, viridis, dplyr, magrittr, DT, igraph, SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph, tidygraph, ggplot2, data.table, sf, concaveman Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: 6232228d73b0e02a1956446377c160e4 NeedsCompilation: no Title: Methods for imaging mass cytometry data analysis Description: This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected. biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering Author: Nils Eling [aut, cre] (), Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb] Maintainer: Nils Eling URL: https://github.com/BodenmillerGroup/imcRtools VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/imcRtools/issues git_url: https://git.bioconductor.org/packages/imcRtools git_branch: RELEASE_3_15 git_last_commit: d7afbb3 git_last_commit_date: 2022-06-04 Date/Publication: 2022-06-05 source.ver: src/contrib/imcRtools_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/imcRtools_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.2/imcRtools_1.2.3.tgz vignettes: vignettes/imcRtools/inst/doc/imcRtools.html vignetteTitles: "Tools for IMC data analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R dependencyCount: 187 Package: IMMAN Version: 1.16.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 0a90c30ab151632824fea262baedc933 NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: RELEASE_3_15 git_last_commit: 92ed93d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IMMAN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMMAN_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMMAN_1.16.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 59 Package: ImmuneSpaceR Version: 1.24.0 Depends: R (>= 3.5.0) Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1), Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots, plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown, preprocessCore, flowCore, flowWorkspace, digest Suggests: knitr, testthat License: GPL-2 MD5sum: 11ed05f3e00f7d39b05c109c510d3f62 NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Database Description: Provides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_15 git_last_commit: cc6ce93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ImmuneSpaceR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ImmuneSpaceR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ImmuneSpaceR_1.24.0.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, interactive_netrc() Function Walkthrough, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R dependencyCount: 139 Package: immunoClust Version: 1.28.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: c64487d3dbcf971068a2b5549f747834 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen [aut, cre] Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: RELEASE_3_15 git_last_commit: aeca556 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/immunoClust_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/immunoClust_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/immunoClust_1.28.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 20 Package: immunotation Version: 1.4.0 Depends: R (>= 4.1) Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr, xml2, maps, rlang Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT License: GPL-3 MD5sum: 927f9c6a6827b9cdba5622985a62cc15 NeedsCompilation: no Title: Tools for working with diverse immune genes Description: MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database. biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics, Annotation Author: Katharina Imkeller [cre, aut] Maintainer: Katharina Imkeller VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: RELEASE_3_15 git_last_commit: 10e45c0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/immunotation_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/immunotation_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/immunotation_1.4.0.tgz vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html vignetteTitles: User guide immunotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R dependencyCount: 68 Package: IMPCdata Version: 1.32.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE Archs: x64 MD5sum: dc5444fc48dfd88289c7659cb5d17840 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: RELEASE_3_15 git_last_commit: a730c65 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IMPCdata_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMPCdata_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMPCdata_1.32.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.70.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 440537591fb255ef4b5f56e1f6d18f37 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: RELEASE_3_15 git_last_commit: 970b2c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/impute_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/impute_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/impute_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, iC10, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, CancerSubtypes, cola, DExMA, doppelgangR, EGAD, fastLiquidAssociation, genefu, genomation, GEOexplorer, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, armada, DIscBIO, FAMT, lilikoi, mi4p, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, DAPAR, graphite, mbOmic, MethPed, MsCoreUtils, QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler, TCGAutils, DDPNA, DGCA, GeoTcgaData, GSA, maGUI, metabolomicsR dependencyCount: 0 Package: INDEED Version: 2.10.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: 7827057246298d74c1b37d57794dc002 NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: RELEASE_3_15 git_last_commit: cddf352 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/INDEED_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INDEED_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INDEED_2.10.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 107 Package: infercnv Version: 1.12.0 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, leiden, reshape, rjags, fitdistrplus, future, foreach, doParallel, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 9c5c6197fccb5e3dca5e082d2cf0491d NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: RELEASE_3_15 git_last_commit: 7d212a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/infercnv_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/infercnv_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/infercnv_1.12.0.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R suggestsMe: SCpubr dependencyCount: 116 Package: infinityFlow Version: 1.6.0 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: 548553d6e6f81b0bef3846e5ab4ed86c NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: RELEASE_3_15 git_last_commit: 9c08b39 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/infinityFlow_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/infinityFlow_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/infinityFlow_1.6.0.tgz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 40 Package: Informeasure Version: 1.6.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, rmarkdown, testthat, SummarizedExperiment License: GPL-3 MD5sum: 3eead6a80da9890d017d81fdda4dab01 NeedsCompilation: no Title: R implementation of Information measures Description: This package compiles most of the information measures currently available: mutual information, conditional mutual information, interaction information, partial information decomposition and part mutual information. All of these estimators can be used to quantify nonlinear dependence between variables in (biological regulatory) network inference. The first estimator is used to infer bivariate networks while the last four estimators are dedicated to analysis of trivariate networks. biocViews: GeneExpression, NetworkInference, Network, Software Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: RELEASE_3_15 git_last_commit: 1f576a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Informeasure_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Informeasure_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Informeasure_1.6.0.tgz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify nonlinear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 1 Package: InPAS Version: 2.4.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq, depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges, GenomicFeatures, ggplot2, IRanges, limma, magrittr,methods,parallelly, plyranges, preprocessCore, readr,reshape2, RSQLite, stats,S4Vectors, utils Suggests: BiocGenerics,BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown, rmarkdown, rtracklayer, RUnit, grDevices, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: d9ce57a55322451caeb0305d5d5be9f8 NeedsCompilation: no Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites. biocViews: Alternative Polyadenylation, Differential Polyadenylation Site Usage, RNA-seq, Gene Regulation, Transcription Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: RELEASE_3_15 git_last_commit: 2de589d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InPAS_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InPAS_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InPAS_2.4.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 149 Package: INPower Version: 1.32.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 7de2de528e03fb61f995efc66161014d NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: RELEASE_3_15 git_last_commit: ebb0687 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/INPower_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INPower_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INPower_1.32.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 3 Package: INSPEcT Version: 1.26.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 03fa9020405df9032a24eebafef7e7ab NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: RELEASE_3_15 git_last_commit: 8ba12ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/INSPEcT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INSPEcT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INSPEcT_1.26.0.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 141 Package: InTAD Version: 1.16.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 89d95e1ca0434c1adf5b6f5827aeebfa NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: RELEASE_3_15 git_last_commit: 8e20580 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InTAD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InTAD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InTAD_1.16.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 135 Package: intansv Version: 1.36.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: 32239f13b705f252ce01119c308a6bdd NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_15 git_last_commit: 0cc0a7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/intansv_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/intansv_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/intansv_1.36.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 157 Package: interacCircos Version: 1.6.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f54821c06b80ff727554c04aa7b9e1e8 NeedsCompilation: no Title: The Generation of Interactive Circos Plot Description: Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework. biocViews: Visualization Author: Zhe Cui [aut, cre] Maintainer: Zhe Cui VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: RELEASE_3_15 git_last_commit: e978f90 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/interacCircos_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interacCircos_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interacCircos_1.6.0.tgz vignettes: vignettes/interacCircos/inst/doc/interacCircos.html vignetteTitles: interacCircos hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R dependencyCount: 14 Package: InteractionSet Version: 1.24.0 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, GenomeInfoDb LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 3e883cd429947dfc2ad35393b57d65c1 NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: RELEASE_3_15 git_last_commit: 5894618 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InteractionSet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InteractionSet_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InteractionSet_1.24.0.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, GenomicInteractions, MACPET, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, extraChIPs, HiCcompare, nullranges, trackViewer suggestsMe: plotgardener, updateObject, CAGEWorkflow dependencyCount: 26 Package: InteractiveComplexHeatmap Version: 1.4.0 Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0) Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer, fontawesome Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges, data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap, cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3, GOexpress, SingleCellExperiment, scater, gplots, pheatmap, airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve (>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks License: MIT + file LICENSE MD5sum: 45a851aaebde81e80a7783d6c8e7a1b1 NeedsCompilation: no Title: Make Interactive Complex Heatmaps Description: This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/InteractiveComplexHeatmap VignetteBuilder: knitr BugReports: https://github.com/jokergoo/InteractiveComplexHeatmap/issues git_url: https://git.bioconductor.org/packages/InteractiveComplexHeatmap git_branch: RELEASE_3_15 git_last_commit: f924ad5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InteractiveComplexHeatmap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InteractiveComplexHeatmap_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InteractiveComplexHeatmap_1.4.0.tgz vignettes: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html, vignettes/InteractiveComplexHeatmap/inst/doc/share.html, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for visualizing DESeq2 results, 7. Implement interactive heatmap from scratch, 2. How interactive complex heatmap is implemented, 5. Interactivate heatmaps indirectly generated by pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize heatmaps interactively, 8. Share interactive heatmaps to collaborators, 3. Functions for Shiny app development hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R, vignettes/InteractiveComplexHeatmap/inst/doc/share.R, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R suggestsMe: simplifyEnrichment dependencyCount: 97 Package: interactiveDisplay Version: 1.34.0 Depends: R (>= 3.5.0), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 Archs: x64 MD5sum: e14b460dd17885e9bbf494e461b85410 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_15 git_last_commit: 0aa2456 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/interactiveDisplay_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interactiveDisplay_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interactiveDisplay_1.34.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 106 Package: interactiveDisplayBase Version: 1.34.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: 1fb62d2338463d7aceb6c68bb8e37cc2 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_15 git_last_commit: fafbb13 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/interactiveDisplayBase_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interactiveDisplayBase_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interactiveDisplayBase_1.34.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: AnnotationHub, interactiveDisplay suggestsMe: recount3 dependencyCount: 43 Package: InterCellar Version: 2.2.0 Depends: R (>= 4.1) Imports: config, golem, shiny, DT, shinydashboard, shinyFiles, shinycssloaders, data.table, fs, dplyr, tidyr, circlize, colourpicker, dendextend, factoextra, ggplot2, plotly, plyr, shinyFeedback, shinyalert, tibble, umap, visNetwork, wordcloud2, readxl, htmlwidgets, colorspace, signal, scales, htmltools, ComplexHeatmap, grDevices, stats, tools, utils, biomaRt, rlang, fmsb, igraph Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, httr License: MIT + file LICENSE MD5sum: dff9dade309518e3a52bf5a6fdfad69b NeedsCompilation: no Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions. biocViews: Software, SingleCell, Visualization, GO, Transcriptomics Author: Marta Interlandi [cre, aut] () Maintainer: Marta Interlandi URL: https://github.com/martaint/InterCellar VignetteBuilder: knitr BugReports: https://github.com/martaint/InterCellar/issues git_url: https://git.bioconductor.org/packages/InterCellar git_branch: RELEASE_3_15 git_last_commit: 4f84b5b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InterCellar_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InterCellar_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InterCellar_2.2.0.tgz vignettes: vignettes/InterCellar/inst/doc/user_guide.html vignetteTitles: InterCellar User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterCellar/inst/doc/user_guide.R dependencyCount: 222 Package: IntEREst Version: 1.20.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 MD5sum: f9d6034bb845805af1bdfe074ea8b02a NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: RELEASE_3_15 git_last_commit: 0a7909c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IntEREst_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IntEREst_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IntEREst_1.20.0.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R dependencyCount: 131 Package: InterMineR Version: 1.18.0 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GO.db, org.Hs.eg.db License: LGPL MD5sum: 9c868fa32013ec63d95fafd8b8787c26 NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis, Celia Sanchez Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_15 git_last_commit: 760e538 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/InterMineR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InterMineR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InterMineR_1.18.0.tgz vignettes: vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/InterMineR.R dependencyCount: 59 Package: IntramiRExploreR Version: 1.18.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: 99b57e09b238304677e4f1f8f4e89f1c NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/IntramiRExploreR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/IntramiRExploreR git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_15 git_last_commit: 8ea4c2a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IntramiRExploreR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IntramiRExploreR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IntramiRExploreR_1.18.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf, vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 33 Package: inveRsion Version: 1.43.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) MD5sum: e2c26288ab84874143456fd19dce9c6d NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres git_url: https://git.bioconductor.org/packages/inveRsion git_branch: master git_last_commit: fba727a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/inveRsion_1.43.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/inveRsion_1.43.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R dependencyCount: 83 Package: IONiseR Version: 2.20.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 5cceb54aa67ac7fffacf25f35c0a1645 NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: RELEASE_3_15 git_last_commit: 6519986 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IONiseR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IONiseR_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IONiseR_2.20.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 90 Package: iPAC Version: 1.40.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 1dd16980240b0060b46274c50f030e52 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: RELEASE_3_15 git_last_commit: 92bcefb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iPAC_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iPAC_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iPAC_1.40.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC dependencyCount: 29 Package: iPath Version: 1.2.0 Depends: R (>= 4.1), mclust, BiocParallel, survival Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer, stats LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, BiocStyle, knitr License: GPL-2 MD5sum: 754b3b451339e73af9446541865f05c7 NeedsCompilation: yes Title: iPath pipeline for detecting perturbed pathways at individual level Description: iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes. biocViews: Pathways, Software, GeneExpression, Survival Author: Kenong Su [aut, cre], Zhaohui Qin [aut] Maintainer: Kenong Su SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/suke18/iPath/issues git_url: https://git.bioconductor.org/packages/iPath git_branch: RELEASE_3_15 git_last_commit: 5770c5d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iPath_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iPath_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iPath_1.2.0.tgz vignettes: vignettes/iPath/inst/doc/iPath.html vignetteTitles: The iPath User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPath/inst/doc/iPath.R dependencyCount: 126 Package: ipdDb Version: 1.14.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 6e37589339e920e558d783525cb12313 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation,AnnotationHubSoftware Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: RELEASE_3_15 git_last_commit: 640c985 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ipdDb_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ipdDb_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ipdDb_1.14.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 87 Package: IPO Version: 1.22.1 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE Archs: x64 MD5sum: ba04706e64c6d676be858379506ef216 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Riebenbauer Maintainer: Thomas Riebenbauer URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: RELEASE_3_15 git_last_commit: b5d205c git_last_commit_date: 2022-06-23 Date/Publication: 2022-08-16 source.ver: src/contrib/IPO_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/IPO_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/IPO_1.22.1.tgz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 129 Package: IRanges Version: 2.30.1 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.39.2), S4Vectors (>= 0.33.3) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 26114c395a867ab76d800fc3aa5f3ef1 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_15 git_last_commit: ead506a git_last_commit_date: 2022-08-17 Date/Publication: 2022-08-18 source.ver: src/contrib/IRanges_2.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/IRanges_2.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/IRanges_2.30.1.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, CODEX, consensusSeekeR, CSAR, CSSQ, customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, epihet, ExCluster, exomeCopy, fCCAC, GenomeInfoDb, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicRanges, groHMM, gtrellis, Gviz, HelloRanges, HiTC, IdeoViz, InTAD, MotifDb, NADfinder, ORFik, OTUbase, pepStat, periodicDNA, plyranges, proBAMr, PSICQUIC, RepViz, rGADEM, rGREAT, RJMCMCNucleosomes, RNAmodR, Scale4C, SCOPE, SGSeq, SICtools, Structstrings, TEQC, traseR, triplex, VariantTools, VplotR, XVector, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, harbChIP, LiebermanAidenHiC2009 importsMe: ALDEx2, AllelicImbalance, alpine, amplican, AneuFinder, annmap, annotatr, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, atena, ballgown, bamsignals, BBCAnalyzer, beadarray, BiocOncoTK, biovizBase, biscuiteer, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, breakpointR, BRGenomics, BSgenome, bsseq, BUMHMM, BumpyMatrix, BUSpaRse, CAGEfightR, cageminer, CAGEr, cBioPortalData, ChIC, ChIPanalyser, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, ChromSCape, chromstaR, chromswitch, chromVAR, cicero, CINdex, circRNAprofiler, cleanUpdTSeq, cleaver, cn.mops, CNEr, CNVfilteR, CNVMetrics, CNVPanelizer, CNVRanger, CNVrd2, COCOA, comapr, coMET, coMethDMR, compEpiTools, ComplexHeatmap, CompoundDb, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprBase, crisprBowtie, crisprScore, CRISPRseek, CrispRVariants, csaw, dada2, DAMEfinder, dasper, debrowser, DECIPHER, deconvR, DegNorm, DelayedMatrixStats, deltaCaptureC, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, dpeak, DRIMSeq, DropletUtils, dStruct, easyRNASeq, EDASeq, eisaR, ELMER, enhancerHomologSearch, EnrichedHeatmap, enrichTF, ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions, epistack, EpiTxDb, epivizr, epivizrData, erma, esATAC, EventPointer, extraChIPs, FastqCleaner, fastseg, fcScan, FilterFFPE, FindIT2, fishpond, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicScores, GenomicTuples, genotypeeval, GenVisR, ggbio, girafe, gmapR, gmoviz, GOfuncR, GOpro, GOTHiC, gpart, GRaNIE, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, hermes, HiCBricks, HiCcompare, HilbertCurve, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, InPAS, INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap, IntEREst, InterMineR, ipdDb, iSEEu, IsoformSwitchAnalyzeR, isomiRs, IVAS, karyoploteR, LinTInd, LOLA, m6Aboost, MACPET, MADSEQ, maser, MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, MesKit, metagene, metagene2, metaseqR2, methimpute, methInheritSim, MethReg, methrix, methylCC, methylInheritance, methylKit, methylPipe, MethylSeekR, methylSig, methylumi, mia, microbiomeMarker, minfi, MinimumDistance, MIRA, missMethyl, MMAPPR2, Modstrings, monaLisa, mosaics, MOSim, Motif2Site, motifbreakR, motifmatchr, MouseFM, msa, MSA2dist, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, mumosa, MungeSumstats, musicatk, MutationalPatterns, NanoStringNCTools, ncRNAtools, normr, nucleoSim, nucleR, nullranges, NxtIRFcore, ODER, OGRE, oligoClasses, OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, panelcn.mops, pcaExplorer, pdInfoBuilder, PhIPData, Pi, PICS, PING, plethy, plotgardener, podkat, polyester, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, profileplyr, ProteoDisco, PureCN, Pviz, QDNAseq, QFeatures, qpgraph, qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, recount, recoup, REDseq, regioneR, regutools, REMP, Repitools, ReportingTools, rfaRm, rfPred, RiboCrypt, RiboDiPA, RiboProfiling, riboSeqR, ribosomeProfilingQC, RIPAT, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, roar, rprimer, Rqc, Rsamtools, RSVSim, RTN, rtracklayer, sarks, SCAN.UPC, scanMiR, scanMiRApp, SCArray, scDblFinder, scHOT, segmenter, segmentSeq, SeqArray, seqCAT, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SimFFPE, SingleMoleculeFootprinting, sitadela, SMITE, snapcount, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, Spectra, spicyR, spiky, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, TreeSummarizedExperiment, TRESS, tricycle, tRNA, tRNAdbImport, tRNAscanImport, tscR, TVTB, txcutr, tximeta, UMI4Cats, Uniquorn, universalmotif, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, VariantFiltering, VaSP, wavClusteR, wiggleplotr, xcms, xcore, XNAString, XVector, yamss, fitCons.UCSC.hg19, GenomicState, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, leeBamViews, MethylSeqData, pd.atdschip.tiling, sesameData, SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS, alakazam, crispRdesignR, geneHapR, geno2proteo, ggcoverage, HiCfeat, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, MitoHEAR, noisyr, numbat, oncoPredict, PACVr, RapidoPGS, RTIGER, Signac, simMP, SNPassoc, STRMPS, tidygenomics, utr.annotation, VALERIE, xQTLbiolinks suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gwascat, GWASTools, HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, regionReport, RTCGA, S4Vectors, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, systemPipeRdata, xcoredata, yeastRNASeq, cancerTiming, fuzzyjoin, gkmSVM, LDheatmap, pagoo, Platypus, polyRAD, rliger, scPloidy, seqmagick, Seurat, sigminer, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: IRISFGM Version: 1.4.0 Depends: R (>= 4.1) Imports: Rcpp (>= 1.0.0), MCL, anocva, Polychrome, RColorBrewer, colorspace, AnnotationDbi, ggplot2, org.Hs.eg.db, org.Mm.eg.db, pheatmap, AdaptGauss, DEsingle,DrImpute, Matrix, Seurat, SingleCellExperiment, clusterProfiler, ggpubr, ggraph, igraph, mixtools, scater, scran, stats, methods, grDevices, graphics, utils, knitr LinkingTo: Rcpp Suggests: rmarkdown License: GPL-2 MD5sum: 35b6df25f577ec17181c627ee57688ef NeedsCompilation: yes Title: Comprehensive Analysis of Gene Interactivity Networks Based on Single-Cell RNA-Seq Description: Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes. biocViews: Software, GeneExpression, SingleCell, Clustering, DifferentialExpression, Preprocessing, DimensionReduction, Visualization, Normalization, DataImport Author: Yuzhou Chang [aut, cre], Qin Ma [aut], Carter Allen [aut], Dongjun Chung [aut] Maintainer: Yuzhou Chang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IRISFGM git_branch: RELEASE_3_15 git_last_commit: c99dcf6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IRISFGM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IRISFGM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IRISFGM_1.4.0.tgz vignettes: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.html vignetteTitles: IRIS-FGM vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.R dependencyCount: 290 Package: ISAnalytics Version: 1.6.2 Depends: R (>= 4.2), magrittr Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, BiocParallel, stringr, fs, lubridate, lifecycle, ggplot2, ggrepel, stats, psych, data.table, readxl, tools, Rcapture, grDevices, forcats, glue, shiny, shinyWidgets, datamods, bslib Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown, roxygen2, vegan, withr, extraDistr, ggalluvial, scales, gridExtra, R.utils, RefManageR, flexdashboard, DT, circlize, plotly, gtools, eulerr, openxlsx License: CC BY 4.0 Archs: x64 MD5sum: ddfb9260eee463b1d307cfae07074342 NeedsCompilation: no Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell Author: Andrea Calabria [aut, cre], Giulio Spinozzi [aut], Giulia Pais [aut] Maintainer: Andrea Calabria URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: RELEASE_3_15 git_last_commit: 27ee9bb git_last_commit_date: 2022-05-23 Date/Publication: 2022-05-24 source.ver: src/contrib/ISAnalytics_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ISAnalytics_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ISAnalytics_1.6.2.tgz vignettes: vignettes/ISAnalytics/inst/doc/ISAnalytics.html, vignettes/ISAnalytics/inst/doc/sharing_analyses.html, vignettes/ISAnalytics/inst/doc/workflow_start.html vignetteTitles: ISAnalytics, sharing_analyses, workflow_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/ISAnalytics.R, vignettes/ISAnalytics/inst/doc/sharing_analyses.R, vignettes/ISAnalytics/inst/doc/workflow_start.R dependencyCount: 110 Package: iSEE Version: 2.8.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, ComplexHeatmap, circlize, grid Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools License: MIT + file LICENSE Archs: x64 MD5sum: e73d44d8c53706517aa45441adfdecb0 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Federico Marini [aut] (), Charlotte Soneson [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEE VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: RELEASE_3_15 git_last_commit: adce814 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iSEE_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEE_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEE_2.8.0.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEu, OSCA.advanced suggestsMe: schex, DuoClustering2018, HCAData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 108 Package: iSEEu Version: 1.8.0 Depends: iSEE Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2, DT, stats, colourpicker, shinyAce Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: 2774023e2fd6fd94d7b500e708572b8c NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] (), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: RELEASE_3_15 git_last_commit: 30041dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iSEEu_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEEu_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEEu_1.8.0.tgz vignettes: vignettes/iSEEu/inst/doc/universe.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/universe.R dependencyCount: 109 Package: iSeq Version: 1.48.0 Depends: R (>= 2.10.0) License: GPL (>= 2) MD5sum: fc6e951549e57aa00b9e31aca7a6d641 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: RELEASE_3_15 git_last_commit: 5b7f2bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iSeq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSeq_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSeq_1.48.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: isobar Version: 1.42.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: e6f046202e46573f6fe0527b837570ca NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: RELEASE_3_15 git_last_commit: 98ed1a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/isobar_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/isobar_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/isobar_1.42.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R suggestsMe: RforProteomics dependencyCount: 92 Package: IsoCorrectoR Version: 1.14.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: af0e853723fe5b8c842a1a26ea039a59 NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: RELEASE_3_15 git_last_commit: e90ecec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IsoCorrectoR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoCorrectoR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoCorrectoR_1.14.0.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 42 Package: IsoCorrectoRGUI Version: 1.12.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 3163f797a0ea7c9622493e606e313c75 NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: RELEASE_3_15 git_last_commit: 8c19187 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IsoCorrectoRGUI_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoCorrectoRGUI_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoCorrectoRGUI_1.12.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 45 Package: IsoformSwitchAnalyzeR Version: 1.18.0 Depends: R (>= 3.6), limma, DEXSeq, ggplot2 Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, DRIMSeq, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl, Biobase Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) MD5sum: 1040494089f317d141f3e10e386a0de9 NeedsCompilation: yes Title: Identify, Annotate and Visualize Alternative Splicing and Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Description: Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup [cre, aut] () Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR git_branch: RELEASE_3_15 git_last_commit: b0f581c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoformSwitchAnalyzeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoformSwitchAnalyzeR_1.18.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R dependencyCount: 161 Package: IsoGeneGUI Version: 2.31.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: f3fff06a60b2ba0c472816b41254e1d6 NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package git_url: https://git.bioconductor.org/packages/IsoGeneGUI git_branch: master git_last_commit: 13831b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IsoGeneGUI_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoGeneGUI_2.31.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoGeneGUI_2.31.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R dependencyCount: 79 Package: ISoLDE Version: 1.24.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: x64 MD5sum: 0b96bc4240987debf875fde64eea3d68 NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: RELEASE_3_15 git_last_commit: 99ecdb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ISoLDE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ISoLDE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ISoLDE_1.24.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.24.1 Depends: R (>= 3.5), SummarizedExperiment Imports: AnnotationDbi, assertive.sets, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, rmarkdown, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 2b343913c3896e6bda2a51e4c10c338a NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP - CIBER Epidemiologia y Salud Publica) Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: RELEASE_3_15 git_last_commit: d4062d4 git_last_commit_date: 2022-08-06 Date/Publication: 2022-08-07 source.ver: src/contrib/isomiRs_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/isomiRs_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/isomiRs_1.24.1.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 151 Package: ITALICS Version: 2.56.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 Archs: x64 MD5sum: 30dd852e867deba83bf1726771b844fb NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: RELEASE_3_15 git_last_commit: 78bd4ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ITALICS_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ITALICS_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ITALICS_2.56.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 59 Package: iterativeBMA Version: 1.54.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: cba3ef478f5cf368a1ce357b62ed6b00 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: RELEASE_3_15 git_last_commit: 5dfdd00 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iterativeBMA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterativeBMA_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterativeBMA_1.54.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 21 Package: iterativeBMAsurv Version: 1.54.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: cf6c172034ce9c3524a7f205b1747109 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: RELEASE_3_15 git_last_commit: a5dd772 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iterativeBMAsurv_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterativeBMAsurv_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterativeBMAsurv_1.54.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: iterClust Version: 1.18.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: 07d46d37dd346fd59e278fd6a5dfed76 NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues git_url: https://git.bioconductor.org/packages/iterClust git_branch: RELEASE_3_15 git_last_commit: f0b4b11 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/iterClust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterClust_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterClust_1.18.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R dependencyCount: 8 Package: iteremoval Version: 1.15.1 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: c2179ff0fa31259a418106976005358c NeedsCompilation: no Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues git_url: https://git.bioconductor.org/packages/iteremoval git_branch: master git_last_commit: ab15019 git_last_commit_date: 2021-11-20 Date/Publication: 2021-11-21 source.ver: src/contrib/iteremoval_1.15.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/iteremoval_1.15.1.tgz vignettes: vignettes/iteremoval/inst/doc/iteremoval.html vignetteTitles: An introduction to iteremoval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R dependencyCount: 53 Package: IVAS Version: 2.16.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: e69a0d5b4396f22cd1dc9e6b51c26c13 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: RELEASE_3_15 git_last_commit: efada2b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IVAS_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IVAS_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IVAS_2.16.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS importsMe: ASpediaFI dependencyCount: 137 Package: ivygapSE Version: 1.18.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat, rmarkdown License: Artistic-2.0 MD5sum: b8fd61493118d7d0b2f8b2d2b60ce37f NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: RELEASE_3_15 git_last_commit: 28c9e40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ivygapSE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ivygapSE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ivygapSE_1.18.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 158 Package: IWTomics Version: 1.20.0 Depends: R (>= 3.5.0), GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 0ff064d202ff8c77843f3fe30e9f208d NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: RELEASE_3_15 git_last_commit: df3386b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/IWTomics_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IWTomics_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IWTomics_1.20.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 70 Package: karyoploteR Version: 1.22.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 MD5sum: 02b2dad6b66de4572f2feab0a699778b NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_15 git_last_commit: c0d3786 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/karyoploteR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/karyoploteR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/karyoploteR_1.22.0.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR, CNViz, multicrispr, RIPAT suggestsMe: Category, MitoHEAR dependencyCount: 148 Package: KBoost Version: 1.4.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 06198c354158aa150c23af61fed1cee8 NeedsCompilation: no Title: Inference of gene regulatory networks from gene expression data Description: Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets. biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference, GeneRegulation, Transcriptomics, SystemsBiology, Transcription, GeneExpression, Regression, PrincipalComponent Author: Luis F. Iglesias-Martinez [aut, cre] (), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: RELEASE_3_15 git_last_commit: 4541ba4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/KBoost_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KBoost_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KBoost_1.4.0.tgz vignettes: vignettes/KBoost/inst/doc/KBoost.html vignetteTitles: KBoost hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KBoost/inst/doc/KBoost.R dependencyCount: 2 Package: KCsmart Version: 2.54.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: a8bf75912495016264c3de50cefa48a0 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: RELEASE_3_15 git_last_commit: 7e88468 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/KCsmart_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KCsmart_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KCsmart_2.54.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 18 Package: kebabs Version: 1.30.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: f4e509f3ffdbe732435c649a3ba9bfbf NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_15 git_last_commit: 90d8024 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/kebabs_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/kebabs_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/kebabs_1.30.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: odseq suggestsMe: spiky dependencyCount: 29 Package: KEGGgraph Version: 1.56.0 Depends: R (>= 3.5.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 087465f27f339008c672f69c7dc0c685 NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_15 git_last_commit: e95cbf9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/KEGGgraph_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGgraph_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGgraph_1.56.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, PFP, iCARH, kangar00, pathfindR suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges dependencyCount: 13 Package: KEGGlincs Version: 1.22.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 Archs: x64 MD5sum: 7a14cf469483a4ca3310c23e7e28148f NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_15 git_last_commit: bbdb95f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/KEGGlincs_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGlincs_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGlincs_1.22.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 60 Package: keggorthology Version: 2.48.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 9f8be51d237eb94ab8ca0c72f7449603 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: RELEASE_3_15 git_last_commit: 7fb88c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/keggorthology_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/keggorthology_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/keggorthology_2.48.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 48 Package: KEGGREST Version: 1.36.3 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr, markdown License: Artistic-2.0 MD5sum: 9b6729663e2f8c61896073607f04c2f8 NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Jeremy Volkening [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_15 git_last_commit: 1827cde git_last_commit_date: 2022-07-08 Date/Publication: 2022-07-12 source.ver: src/contrib/KEGGREST_1.36.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGREST_1.36.3.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGREST_1.36.3.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA, g2f, pathfindR suggestsMe: Category, categoryCompare, GenomicRanges, globaltest, iSEEu, MLP, padma, RTopper, CALANGO, maGUI, ptm, scDiffCom dependencyCount: 27 Package: KinSwingR Version: 1.14.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 27a6b69c915bd717e3cfa5223fc91e61 NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: RELEASE_3_15 git_last_commit: b272ad8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/KinSwingR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KinSwingR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KinSwingR_1.14.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 35 Package: kissDE Version: 1.16.0 Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel, shiny, shinycssloaders, ade4, factoextra, DT Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: ff2f149def7dca58faa94846e9543197 NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_15 git_last_commit: 5a6e6f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/kissDE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/kissDE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/kissDE_1.16.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 215 Package: KnowSeq Version: 1.10.2 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) MD5sum: b8f390f1382c6a3fb8e7dac21feec7e6 NeedsCompilation: no Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: RELEASE_3_15 git_last_commit: 668c620 git_last_commit_date: 2022-07-01 Date/Publication: 2022-07-03 source.ver: src/contrib/KnowSeq_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/KnowSeq_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/KnowSeq_1.10.2.tgz vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html vignetteTitles: The KnowSeq users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R dependencyCount: 172 Package: LACE Version: 2.0.0 Depends: R (>= 4.1.0) Imports: curl, igraph, foreach, doParallel, sortable, dplyr, data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils, purrr, stringi, stringr, Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs, data.table, htmltools, htmlwidgets, bsplus, shiny, shinythemes, shinyFiles, shinyjs, shinyBS, shinydashboard, biomaRt, callr Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown License: file LICENSE MD5sum: a01f83d968c1846fe5b4400036e0aeac NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (), Gianluca Ascolani [aut] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: RELEASE_3_15 git_last_commit: 2ce5339 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LACE_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LACE_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LACE_2.0.0.tgz vignettes: vignettes/LACE/inst/doc/LACE1.pdf, vignettes/LACE/inst/doc/LACE2.pdf vignetteTitles: LACE, LACE 2.0 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/LACE1.R, vignettes/LACE/inst/doc/LACE2.R dependencyCount: 143 Package: lapmix Version: 1.62.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 8f761ffe92484a85951863868d442d01 NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch git_url: https://git.bioconductor.org/packages/lapmix git_branch: RELEASE_3_15 git_last_commit: a47330c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lapmix_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lapmix_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lapmix_1.62.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R dependencyCount: 8 Package: LBE Version: 1.64.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: e099700e16eb9acf5e09e31a464d90b6 NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: RELEASE_3_15 git_last_commit: 8bcc58d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LBE_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LBE_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LBE_1.64.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependsOnMe: PhViD dependencyCount: 5 Package: ldblock Version: 1.26.0 Depends: R (>= 3.5), methods Imports: Matrix, snpStats, VariantAnnotation, GenomeInfoDb, httr, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6), BiocGenerics (>= 0.25.1) Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown License: Artistic-2.0 MD5sum: e8cd9999e38559601a05286ec90665d2 NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: RELEASE_3_15 git_last_commit: 7a5cd9d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ldblock_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ldblock_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ldblock_1.26.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R dependencyCount: 108 Package: LEA Version: 3.8.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: a4558adcf87818917757ca9ede6a860a NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset based on new or predicted environments. The package includes factor methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois , Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: RELEASE_3_15 git_last_commit: 2d0dc86 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LEA_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LEA_3.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LEA_3.8.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R dependencyCount: 4 Package: LedPred Version: 1.30.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 3cc38017660ac1bcba10157739acbe30 NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_15 git_last_commit: 5700488 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LedPred_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LedPred_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LedPred_1.30.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 74 Package: lefser Version: 1.6.0 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, stats, methods Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, testthat, pkgdown, covr, withr License: Artistic-2.0 Archs: x64 MD5sum: 08f7a0c6f4429f7fd71713eba89e9e99 NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is an implementation in R of the popular "LDA Effect Size (LEfSe)" method for microbiome biomarker discovery. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups and sub-groups. biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Asya Khleborodova [cre, aut], Ludwig Geistlinger [ctb], Marcel Ramos [ctb] (), Levi Waldron [ctb] Maintainer: Asya Khleborodova URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: RELEASE_3_15 git_last_commit: dd0b784 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lefser_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lefser_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lefser_1.6.0.tgz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Introduction to the lefser R implementation of the popular LEfSE software for biomarker discovery in microbiome analysis. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R dependencyCount: 64 Package: les Version: 1.46.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 97c5dbed45fed523d2711ed9fe32072d NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: RELEASE_3_15 git_last_commit: dccbe3a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/les_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/les_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/les_1.46.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.14.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods, rmarkdown LinkingTo: Rcpp Suggests: rmarkdown License: GPL (>= 2) MD5sum: 30ab7fc7e9038c3fb057f701351c5f76 NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_15 git_last_commit: 4046cbe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/levi_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/levi_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/levi_1.14.0.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 99 Package: lfa Version: 1.26.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 MD5sum: f05f636d1a1da7638f4443e6291a6124 NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: RELEASE_3_15 git_last_commit: bbebe2a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lfa_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lfa_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lfa_1.26.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest suggestsMe: popkin dependencyCount: 2 Package: limma Version: 3.52.4 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: x64 MD5sum: 10c805a4c9ecdd77809e1e0e5e3d988b NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_15 git_last_commit: 3226c29 git_last_commit_date: 2022-09-26 Date/Publication: 2022-09-27 source.ver: src/contrib/limma_3.52.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/limma_3.52.4.zip mac.binary.ver: bin/macosx/contrib/4.2/limma_3.52.4.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, deco, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, GEOexplorer, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR2, mpra, NeuCA, protGear, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, splineTimeR, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123, OSCA.advanced, OSCA.basic, OSCA.workflows, BALLI, BioInsight, CEDA, countTransformers, cp4p, DAAGbio, DRomics, fmt, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ASpediaFI, ATACseqQC, attract, autonomics, AWFisher, ballgown, BatchQC, beadarray, benchdamic, biotmle, BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, CancerSubtypes, casper, ChAMP, clusterExperiment, CNVRanger, combi, compcodeR, consensusDE, consensusOV, crlmm, crossmeta, csaw, cTRAP, ctsGE, CytoTree, DAMEfinder, DaMiRseq, debrowser, DEP, derfinderPlot, DEsubs, DExMA, DiffBind, diffcyt, diffHic, diffloop, diffUTR, distinct, DMRcate, Doscheda, DRIMSeq, eegc, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, erccdashboard, EventPointer, EWCE, ExploreModelMatrix, extraChIPs, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, Glimma, GOsummaries, GRaNIE, hermes, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MatrixQCvis, MBECS, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, microbiomeMarker, MIGSA, miloR, minfi, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, NanoMethViz, NanoTube, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PanomiR, PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenoTest, PhosR, polyester, POMA, POWSC, projectR, psichomics, pwrEWAS, qPLEXanalyzer, qsea, RegEnrich, regsplice, Ringo, RNAinteract, ROSeq, RTCGAToolbox, RTN, RTopper, satuRn, scClassify, scone, scran, SEPIRA, seqsetvis, shinyepico, SimBindProfiles, SingleCellSignalR, singleCellTK, snapCGH, sparrow, spatialHeatmap, SPsimSeq, standR, STATegRa, sva, timecourse, TimeSeriesExperiment, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, CIDER, cinaR, DiPALM, dsb, easyDifferentialGeneCoexpression, GWASbyCluster, INCATome, lilikoi, limorhyde2, lipidomeR, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, MSclassifR, nlcv, Patterns, plfMA, promor, RANKS, RPPanalyzer, scBio, scRNAtools, ssizeRNA, statVisual, tinyarray, wrProteo suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocSet, BioNet, BioQC, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, DAPAR, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, EnMCB, fgsea, fishpond, gage, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, NxtIRFcore, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, qmtools, qsvaR, randRotation, Rcade, recountmethylation, ribosomeProfilingQC, rtracklayer, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, DGEobj.utils, dnet, GeoTcgaData, hexbin, limorhyde, LPS, maGUI, NACHO, Platypus, propr, protti, seqgendiff, Seurat, simphony, st, volcano3D, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 5 Package: limmaGUI Version: 1.72.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) Archs: x64 MD5sum: 193a5e89e3a4616b0d66fb3e9adef1b8 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: RELEASE_3_15 git_last_commit: bc11ee8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/limmaGUI_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/limmaGUI_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/limmaGUI_1.72.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 10 Package: LineagePulse Version: 1.16.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 Archs: x64 MD5sum: 919659f055b12b3058e8dd88ff8f0973 NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues git_url: https://git.bioconductor.org/packages/LineagePulse git_branch: RELEASE_3_15 git_last_commit: 6bd03f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LineagePulse_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LineagePulse_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LineagePulse_1.16.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R dependencyCount: 88 Package: lineagespot Version: 1.0.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 428a607766d413ea038010c9d6c3e8da NeedsCompilation: no Title: Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing Description: Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages. biocViews: VariantDetection, VariantAnnotation, Sequencing Author: Nikolaos Pechlivanis [aut, cre] (), Maria Tsagiopoulou [aut], Maria Christina Maniou [aut], Anastasis Togkousidis [aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris [aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut], Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris Karapantsios [aut], Stamatia Laidou [aut], Elisavet Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E. Psomopoulos [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/lineagespot VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues git_url: https://git.bioconductor.org/packages/lineagespot git_branch: RELEASE_3_15 git_last_commit: 97c96e7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lineagespot_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lineagespot_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lineagespot_1.0.0.tgz vignettes: vignettes/lineagespot/inst/doc/lineagespot.html vignetteTitles: lineagespot User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R dependencyCount: 100 Package: LinkHD Version: 1.10.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 6b972ad98369a37802e17e9fbade3bfe NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: RELEASE_3_15 git_last_commit: baa3721 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LinkHD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LinkHD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LinkHD_1.10.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 142 Package: Linnorm Version: 2.20.0 Depends: R(>= 4.1.0) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE MD5sum: 05ac771a6d8afaae0137b7176aeb369e NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_15 git_last_commit: 9ed9112 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Linnorm_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Linnorm_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Linnorm_2.20.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem dependencyCount: 67 Package: LinTInd Version: 1.0.0 Depends: R (>= 4.0), ggplot2, parallel, stats, S4Vectors Imports: data.tree, reshape2, networkD3, stringdist, purrr, ape, cowplot, ggnewscale, stringr, dplyr, rlist, pheatmap, Biostrings, IRanges, BiocGenerics(>= 0.36.1), ggtree Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 86ce1c0b4fea67af017f42748a6d135e NeedsCompilation: no Title: Lineage tracing by indels Description: When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagProcess() function will help you to extract indels for each cell or read. TagDist() function will help you to calculate the similarity between each pair of groups across the indwells they contain. BuildTree() function will help you to reconstruct a tree. PlotTree() function will help you to visualize the tree. biocViews: SingleCell, CRISPR, Alignment Author: Luyue Wang [aut, cre], Bin Xiang [ctb], Hengxin Liu [ctb], Wu Wei [ths] Maintainer: Luyue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinTInd git_branch: RELEASE_3_15 git_last_commit: f035997 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LinTInd_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LinTInd_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LinTInd_1.0.0.tgz vignettes: vignettes/LinTInd/inst/doc/tutorial.html vignetteTitles: LinTInd - tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LinTInd/inst/doc/tutorial.R dependencyCount: 89 Package: lionessR Version: 1.10.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE Archs: x64 MD5sum: 9372fefbfcc4c4b8069e840d6f4e6b47 NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (), Ping-Han Hsieh [cre] () Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: RELEASE_3_15 git_last_commit: 1f93ae5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lionessR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lionessR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lionessR_1.10.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 25 Package: lipidr Version: 2.10.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, iheatmapr, spelling, testthat License: MIT + file LICENSE MD5sum: 3ce034ced23dda48789f57e77c52cd7c NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: RELEASE_3_15 git_last_commit: 6895710 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lipidr_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lipidr_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lipidr_2.10.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R suggestsMe: rgoslin dependencyCount: 91 Package: LiquidAssociation Version: 1.50.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 28783a5f49f0c472f165550fcb05d4a7 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: RELEASE_3_15 git_last_commit: e22b44f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LiquidAssociation_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LiquidAssociation_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LiquidAssociation_1.50.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 84 Package: lisaClust Version: 1.4.0 Depends: R (>= 4.1) Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.core, spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr, stats, data.table, dplyr, tidyr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: e1f2fbbe81afc8eae6c083e728a67568 NeedsCompilation: no Title: lisaClust: Clustering of Local Indicators of Spatial Association Description: lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution. biocViews: SingleCell, CellBasedAssays Author: Ellis Patrick [aut, cre], Nicolas Canete [aut] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/lisaClust/issues git_url: https://git.bioconductor.org/packages/lisaClust git_branch: RELEASE_3_15 git_last_commit: 86f2d70 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lisaClust_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lisaClust_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lisaClust_1.4.0.tgz vignettes: vignettes/lisaClust/inst/doc/lisaClust.html vignetteTitles: "Inroduction to lisaClust" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R dependencyCount: 110 Package: lmdme Version: 1.38.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 0ca80fa6aede9969226c8aa3e099f604 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: RELEASE_3_15 git_last_commit: 80d7b3e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lmdme_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lmdme_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lmdme_1.38.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 8 Package: LOBSTAHS Version: 1.22.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 7cbd18f5b385545d0876c40712227e9b NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut], Daniel Lowenstein [aut] Maintainer: Henry Holm , Daniel Lowenstein , James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: RELEASE_3_15 git_last_commit: 5b2d845 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LOBSTAHS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LOBSTAHS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LOBSTAHS_1.22.0.tgz vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependsOnMe: PtH2O2lipids dependencyCount: 127 Package: loci2path Version: 1.16.0 Depends: R (>= 3.5.0) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 647033081c8c8f682eeb8600c4e3b45a NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: RELEASE_3_15 git_last_commit: 21f2dc3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/loci2path_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/loci2path_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/loci2path_1.16.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 44 Package: logicFS Version: 2.16.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 7e4c7d8bdae528f8f3d781cc71140d5b NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: RELEASE_3_15 git_last_commit: b3c5ab0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/logicFS_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/logicFS_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/logicFS_2.16.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: logitT Version: 1.54.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) MD5sum: b70b215e087b1b7d57622e2c037d7cb8 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/logitT git_branch: RELEASE_3_15 git_last_commit: 9428862 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/logitT_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/logitT_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/logitT_1.54.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R dependencyCount: 12 Package: LOLA Version: 1.26.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 MD5sum: 924e261516ae26e395ade612fefd46b8 NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: RELEASE_3_15 git_last_commit: eb6404f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LOLA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LOLA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LOLA_1.26.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, DeepBlueR, MAGAR, MIRA, ramr dependencyCount: 24 Package: LoomExperiment Version: 1.14.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, BiocIO Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate License: Artistic-2.0 Archs: x64 MD5sum: 31e44a97545a5065a92d8e018bc5a9b7 NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: RELEASE_3_15 git_last_commit: 3056093 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LoomExperiment_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LoomExperiment_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LoomExperiment_1.14.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R dependsOnMe: OSCA.intro suggestsMe: hca dependencyCount: 35 Package: LowMACA Version: 1.26.2 Depends: R (>= 2.10) Imports: cBioPortalData, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase, plyr Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: b61d69b49f3b9649d0cc11319ae8ad74 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Giorgio Melloni , Stefano de Pretis Maintainer: Giorgio Melloni , Stefano de Pretis SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_15 git_last_commit: 96c7696 git_last_commit_date: 2022-10-13 Date/Publication: 2022-10-16 source.ver: src/contrib/LowMACA_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/LowMACA_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/LowMACA_1.26.2.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R dependencyCount: 160 Package: LPE Version: 1.70.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 528f2cabcf346d6402f747f7d227746c NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: RELEASE_3_15 git_last_commit: 3c07473 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LPE_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LPE_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LPE_1.70.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray dependencyCount: 1 Package: LPEadj Version: 1.56.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 41a298caf492cd79bf27a82a985a7b7c NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie git_url: https://git.bioconductor.org/packages/LPEadj git_branch: RELEASE_3_15 git_last_commit: 2a4d45c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LPEadj_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LPEadj_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LPEadj_1.56.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R dependencyCount: 2 Package: lpNet Version: 2.28.0 Depends: lpSolve License: Artistic License 2.0 MD5sum: ddbdb1d86506162b87944a30e214d24d NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: RELEASE_3_15 git_last_commit: 9182bd5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lpNet_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lpNet_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lpNet_2.28.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 1 Package: lpsymphony Version: 1.24.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: e434b5054e2d78346b23afd6f0c8bd80 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb] Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: RELEASE_3_15 git_last_commit: a9f9553 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lpsymphony_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lpsymphony_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lpsymphony_1.24.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW, Maaslin2 suggestsMe: oppr, prioritizr, TestDesign dependencyCount: 0 Package: LRBaseDbi Version: 2.6.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle License: Artistic-2.0 MD5sum: 3d7d770feb4c4a3a7be8479230b57b57 NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: RELEASE_3_15 git_last_commit: e50565a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LRBaseDbi_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LRBaseDbi_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LRBaseDbi_2.6.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 45 Package: LRcell Version: 1.4.0 Depends: R (>= 4.1), ExperimentHub, AnnotationHub Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 70de67c4d39865d04c4f757c223a0bb9 NeedsCompilation: no Title: Differential cell type change analysis using Logistic/linear Regression Description: The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus). biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression, GeneExpression, DifferentialExpression Author: Wenjing Ma [cre, aut] () Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: RELEASE_3_15 git_last_commit: f7ee009 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LRcell_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LRcell_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LRcell_1.4.0.tgz vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html vignetteTitles: LRcell Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R suggestsMe: LRcellTypeMarkers dependencyCount: 114 Package: lumi Version: 2.48.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: b040515da74a81fec356088656cbbe97 NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/lumi git_branch: RELEASE_3_15 git_last_commit: 1f988ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/lumi_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lumi_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lumi_2.48.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData importsMe: arrayMvout, ffpe, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, maGUI dependencyCount: 162 Package: LymphoSeq Version: 1.24.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: 2cd4e00e0c113568ca534bc73d929bfd NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: RELEASE_3_15 git_last_commit: 19c829d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/LymphoSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LymphoSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LymphoSeq_1.24.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 91 Package: M3C Version: 1.18.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: 9e5057e9a8c5118a89eb3bce0e47b43d NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: RELEASE_3_15 git_last_commit: d1a42a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/M3C_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/M3C_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/M3C_1.18.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: HCV, lilikoi suggestsMe: metabolomicsR, parameters dependencyCount: 61 Package: M3Drop Version: 1.22.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment, monocle, Seurat, Biobase License: GPL (>=2) MD5sum: 84fb589f2c5b684665c29d219b961bcb NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: RELEASE_3_15 git_last_commit: 251aea9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/M3Drop_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/M3Drop_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/M3Drop_1.22.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 102 Package: m6Aboost Version: 1.2.0 Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1) Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods, IRanges, ExperimentHub Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: edd15ec7ed007d05e5305f0c6414d53e NeedsCompilation: no Title: m6Aboost Description: This package can help user to run the m6Aboost model on their own miCLIP2 data. The package includes functions to assign the read counts and get the features to run the m6Aboost model. The miCLIP2 data should be stored in a GRanges object. More details can be found in the vignette. biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware Author: You Zhou [aut, cre] (), Kathi Zarnack [aut] () Maintainer: You Zhou URL: https://github.com/ZarnackGroup/m6Aboost VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/m6Aboost/issues git_url: https://git.bioconductor.org/packages/m6Aboost git_branch: RELEASE_3_15 git_last_commit: 7b19574 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/m6Aboost_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/m6Aboost_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/m6Aboost_1.2.0.tgz vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html vignetteTitles: m6Aboost Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R dependencyCount: 164 Package: maanova Version: 1.66.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) MD5sum: dea8dac7ecd1cb725cb18560694918a5 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill git_url: https://git.bioconductor.org/packages/maanova git_branch: RELEASE_3_15 git_last_commit: 006daa1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maanova_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maanova_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maanova_1.66.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: Maaslin2 Version: 1.10.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4 Suggests: knitr, testthat (>= 2.1.0), rmarkdown License: MIT + file LICENSE MD5sum: b42f7125a7924bc2090da08a3d2236e1 NeedsCompilation: no Title: "Multivariable Association Discovery in Population-scale Meta-omics Studies" Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: RELEASE_3_15 git_last_commit: 540229e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Maaslin2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Maaslin2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Maaslin2_1.10.0.tgz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: MaAsLin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: Macarron, MMUPHin dependencyCount: 144 Package: Macarron Version: 1.0.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table, dynamicTreeCut, Maaslin2, plyr, stats, psych, xml2, RCurl, RJSONIO, logging, methods, utils Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE MD5sum: a1a1c9a149b115859535e63171f1775f NeedsCompilation: no Title: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets Description: Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition. biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction, Clustering Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/macarron VignetteBuilder: knitr BugReports: https://forum.biobakery.org/c/microbial-community-profiling/macarron git_url: https://git.bioconductor.org/packages/Macarron git_branch: RELEASE_3_15 git_last_commit: 8c876dd git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/Macarron_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Macarron_1.0.0.tgz vignettes: vignettes/Macarron/inst/doc/MACARRoN.html vignetteTitles: Macarron hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Macarron/inst/doc/MACARRoN.R dependencyCount: 207 Package: macat Version: 1.70.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 Archs: x64 MD5sum: f937dae3daea904d7bb63142a58060d7 NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling git_url: https://git.bioconductor.org/packages/macat git_branch: RELEASE_3_15 git_last_commit: ea517ed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/macat_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/macat_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/macat_1.70.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R dependencyCount: 48 Package: maCorrPlot Version: 1.66.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 9298174c8362112046601225fa131843 NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: RELEASE_3_15 git_last_commit: 1f31f7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maCorrPlot_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maCorrPlot_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maCorrPlot_1.66.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACPET Version: 1.15.1 Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>= 4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3), stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1), GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>= 2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1), GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>= 1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0), GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>= 1.43.0), futile.logger (>= 1.4.3) LinkingTo: Rcpp, bigmemory, BH Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14), reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2) License: GPL-3 MD5sum: e4a0f61bfcc279bd9988a010676ac203 NeedsCompilation: yes Title: Model based analysis for paired-end data Description: The MACPET package can be used for complete interaction analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. Finally it runs an additive interaction-analysis model for calling for significant interactions between those peaks. MACPET is mainly written in C++, and it also supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACPET git_branch: master git_last_commit: 2da9d25 git_last_commit_date: 2021-11-20 Date/Publication: 2021-12-05 source.ver: src/contrib/MACPET_1.15.1.tar.gz vignettes: vignettes/MACPET/inst/doc/MACPET.pdf vignetteTitles: MACPET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACPET/inst/doc/MACPET.R dependencyCount: 107 Package: MACSQuantifyR Version: 1.10.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: 79013bb5152d417b0c468b63df53b4d2 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: RELEASE_3_15 git_last_commit: 4da91f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MACSQuantifyR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MACSQuantifyR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MACSQuantifyR_1.10.0.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 85 Package: MACSr Version: 1.4.0 Depends: R (>= 4.1.0) Imports: utils, reticulate, S4Vectors, methods, basilisk, ExperimentHub, AnnotationHub Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata License: BSD_3_clause + file LICENSE MD5sum: ee029dd766acf842e40ea9d4032697cc NeedsCompilation: no Title: MACS: Model-based Analysis for ChIP-Seq Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3. biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology Author: Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSr git_branch: RELEASE_3_15 git_last_commit: e4327db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MACSr_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/MACSr_1.4.0.tgz vignettes: vignettes/MACSr/inst/doc/MACSr.html vignetteTitles: MACSr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MACSr/inst/doc/MACSr.R dependencyCount: 98 Package: made4 Version: 1.70.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1dbbafeaac081995236e5a042c69d381 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: RELEASE_3_15 git_last_commit: 3b1d510 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/made4_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/made4_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/made4_1.70.0.tgz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: deco, omicade4 dependencyCount: 35 Package: MADSEQ Version: 1.22.0 Depends: R (>= 3.5.0), rjags (>= 4.6) Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) MD5sum: 5c36d229a767d56ad2e79b6c8f65fe7b NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues git_url: https://git.bioconductor.org/packages/MADSEQ git_branch: RELEASE_3_15 git_last_commit: 25b8bbd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MADSEQ_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MADSEQ_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MADSEQ_1.22.0.tgz vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R dependencyCount: 116 Package: maftools Version: 2.12.0 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy LinkingTo: Rhtslib, zlibbioc Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors, pheatmap, curl License: MIT + file LICENSE MD5sum: 1b57c890f6733bf3db31dafa61eeb0d3 NeedsCompilation: yes Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] () Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_15 git_last_commit: 0ffc97e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maftools_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maftools_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maftools_2.12.0.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/cnv_analysis.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R, vignettes/maftools/inst/doc/cnv_analysis.R, vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R importsMe: CIMICE, musicatk, TCGAbiolinksGUI, TCGAWorkflow, oncoPredict, pathwayTMB, PMAPscore, Rediscover, sigminer, SMDIC suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 15 Package: MAGAR Version: 1.4.0 Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table, UpSetR, reshape2, jsonlite, methods, ff, argparse, impute, RnBeads.hg19, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 MD5sum: b11b95ad4b7502c2d5893d03d0647009 NeedsCompilation: no Title: MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data Description: "Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs. biocViews: Regression, Epigenetics, DNAMethylation, SNP, GeneticVariability, MethylationArray, Microarray, CpGIsland, MethylSeq, Sequencing, mRNAMicroarray, Preprocessing, CopyNumberVariation, TwoChannel, ImmunoOncology, DifferentialMethylation, BatchEffect, QualityControl, DataImport, Network, Clustering, GraphAndNetwork Author: Michael Scherer [cre, aut] () Maintainer: Michael Scherer URL: https://github.com/MPIIComputationalEpigenetics/MAGAR VignetteBuilder: knitr BugReports: https://github.com/MPIIComputationalEpigenetics/MAGAR/issues git_url: https://git.bioconductor.org/packages/MAGAR git_branch: RELEASE_3_15 git_last_commit: 4596003 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MAGAR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAGAR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAGAR_1.4.0.tgz vignettes: vignettes/MAGAR/inst/doc/MAGAR.html vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R dependencyCount: 197 Package: MAGeCKFlute Version: 2.0.0 Depends: R (>= 4.1) Imports: Biobase, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils, DOSE, clusterProfiler, pathview, enrichplot, msigdbr, depmap Suggests: biomaRt, BiocStyle, dendextend, graphics, knitr, pheatmap, png, scales, sva, BiocManager License: GPL (>=3) Archs: x64 MD5sum: 2262894e77c9ec99695e75f1dec06c6b NeedsCompilation: no Title: Integrative Analysis Pipeline for Pooled CRISPR Functional Genetic Screens Description: CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl, Normalization, GeneSetEnrichment, Pathways, Visualization, GeneTarget, KEGG Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu Maintainer: Wubing Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: RELEASE_3_15 git_last_commit: 2a3e1ee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MAGeCKFlute_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAGeCKFlute_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAGeCKFlute_2.0.0.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R dependencyCount: 164 Package: MAI Version: 1.2.0 Depends: R (>= 3.5.0) Imports: caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: a5c07e32704252c0a726bab640bed98e NeedsCompilation: no Title: Mechanism-Aware Imputation Description: A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present. biocViews: Software, Metabolomics, StatisticalMethod, Classification Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut] Maintainer: Jonathan Dekermanjian URL: https://github.com/KechrisLab/MAI VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MAI/issues git_url: https://git.bioconductor.org/packages/MAI git_branch: RELEASE_3_15 git_last_commit: 44b1f94 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MAI_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAI_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAI_1.2.0.tgz vignettes: vignettes/MAI/inst/doc/UsingMAI.html vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MAI/inst/doc/UsingMAI.R dependencyCount: 165 Package: maigesPack Version: 1.60.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) MD5sum: 66c009995c4f0b07c021df3cbf895464 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ git_url: https://git.bioconductor.org/packages/maigesPack git_branch: RELEASE_3_15 git_last_commit: 73e313e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maigesPack_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maigesPack_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maigesPack_1.60.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R dependencyCount: 12 Package: MAIT Version: 1.30.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: f38ce1f8571fc33caa3c861545fef8f9 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: RELEASE_3_15 git_last_commit: 8f504b7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MAIT_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAIT_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAIT_1.30.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R dependencyCount: 212 Package: makecdfenv Version: 1.72.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: x64 MD5sum: 39124bea889c7c8106a86ab7428a195f NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: RELEASE_3_15 git_last_commit: 85c8968 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/makecdfenv_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/makecdfenv_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/makecdfenv_1.72.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 12 Package: MANOR Version: 1.68.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: 1603d548dbf5567fa08741692331f7d6 NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: RELEASE_3_15 git_last_commit: 22ec098 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MANOR_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MANOR_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MANOR_1.68.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.66.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) Archs: x64 MD5sum: 3f9b77697cbc033aaf11ee4f46d8ecff NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: RELEASE_3_15 git_last_commit: 8fe82a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MantelCorr_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MantelCorr_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MantelCorr_1.66.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: mAPKL Version: 1.26.0 Depends: R (>= 3.6.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: dd069b8912054fcae9675f356eb62aef NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mAPKL git_branch: RELEASE_3_15 git_last_commit: 03071bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mAPKL_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mAPKL_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mAPKL_1.26.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R dependencyCount: 67 Package: maPredictDSC Version: 1.34.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 2d1990543c63eb4cabb33f01e2dc1c5d NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: RELEASE_3_15 git_last_commit: e7115cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maPredictDSC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maPredictDSC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maPredictDSC_1.34.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 131 Package: mapscape Version: 1.20.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d17d270ea32e973877b429118a347844 NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: RELEASE_3_15 git_last_commit: 0f6ced0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mapscape_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mapscape_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mapscape_1.20.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 17 Package: marr Version: 1.6.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) MD5sum: e20313cfa11ca87d03971cd766ad8a24 NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: RELEASE_3_15 git_last_commit: 18221c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/marr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/marr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/marr_1.6.0.tgz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 57 Package: marray Version: 1.74.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 541361a562d906aafb5c9a87d649f1c6 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: RELEASE_3_15 git_last_commit: 9130a93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/marray_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/marray_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/marray_1.74.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 6 Package: martini Version: 1.16.0 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils, LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>= 2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>= 0.12.2), memoise (>= 2.0.0), knitr, testthat, readr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 72013fce5c2c853d8eb00808e6671a77 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (), Chloe-Agathe Azencott [aut] () Maintainer: Hector Climente-Gonzalez URL: https://github.com/hclimente/martini VignetteBuilder: knitr BugReports: https://github.com/hclimente/martini/issues git_url: https://git.bioconductor.org/packages/martini git_branch: RELEASE_3_15 git_last_commit: 36d78b0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/martini_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/martini_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/martini_1.16.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 19 Package: maser Version: 1.14.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: 8977c47de7fb7fd8ee6d404de7372351 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: RELEASE_3_15 git_last_commit: c6e1d3b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maser_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maser_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maser_1.14.0.tgz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 153 Package: maSigPro Version: 1.68.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) Archs: x64 MD5sum: 376c44a397921f8b4c593af936c07ebc NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa and Maria Jose Nueda Maintainer: Maria Jose Nueda git_url: https://git.bioconductor.org/packages/maSigPro git_branch: RELEASE_3_15 git_last_commit: 3ef327d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maSigPro_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maSigPro_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maSigPro_1.68.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: maskBAD Version: 1.40.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: 1e095455bdb5d6ce1d996e70e30db51a NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: RELEASE_3_15 git_last_commit: 0b5e50c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/maskBAD_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maskBAD_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maskBAD_1.40.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 25 Package: MassArray Version: 1.48.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 30fca9bd45411938ce1acab371d1e6f6 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: RELEASE_3_15 git_last_commit: a63df68 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MassArray_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MassArray_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MassArray_1.48.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.32.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 7ca35fdd9806ede631ddcc42d6820a7a NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: RELEASE_3_15 git_last_commit: 1495c02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/massiR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/massiR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/massiR_1.32.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 14 Package: MassSpecWavelet Version: 1.62.0 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown License: LGPL (>= 2) MD5sum: 9ac7698c6c206e751b8edb19ff326fc0 NeedsCompilation: no Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Pan Du [aut], Warren Kibbe [aut], Simon Lin [aut], Sergio Oller Moreno [cre] () Maintainer: Sergio Oller Moreno URL: https://github.com/zeehio/MassSpecWavelet VignetteBuilder: knitr BugReports: http://github.com/zeehio/MassSpecWavelet/issues git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_15 git_last_commit: 12e47e6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MassSpecWavelet_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MassSpecWavelet_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MassSpecWavelet_1.62.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html vignetteTitles: Using the MassSpecWavelet package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, Rnmr1D, speaq suggestsMe: downlit dependencyCount: 0 Package: MAST Version: 1.22.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, Matrix, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: 5f297b1e8681b3f840c4903557606dd5 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: RELEASE_3_15 git_last_commit: d9fd0c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MAST_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAST_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAST_1.22.0.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R dependsOnMe: POWSC importsMe: benchdamic, celaref, singleCellTK, CSCDRNA, DWLS, PALMO suggestsMe: clusterExperiment, EWCE, Seurat dependencyCount: 67 Package: matchBox Version: 1.38.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 7b009d0643c13e94b10366fe279069a9 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: RELEASE_3_15 git_last_commit: ae19b40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/matchBox_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/matchBox_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/matchBox_1.38.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.8.1 Depends: matrixStats (>= 0.60.1) Imports: methods Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0), Matrix License: Artistic-2.0 MD5sum: 1c67f2f5d160e39543e9ccd2c7e51330 NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (), Peter Hickey [aut, cre] (), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: RELEASE_3_15 git_last_commit: a4a2108 git_last_commit_date: 2022-06-23 Date/Publication: 2022-06-26 source.ver: src/contrib/MatrixGenerics_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixGenerics_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixGenerics_1.8.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: CoreGx, escape, lineagespot, miaSim, MinimumDistance, PDATK, RaggedExperiment, scone, scPCA, tLOH, transformGamPoi, VanillaICE suggestsMe: MungeSumstats dependencyCount: 2 Package: MatrixQCvis Version: 1.4.0 Depends: SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>= 3.47.12), methods (>= 4.1.0), openxlsx (>= 4.2.3), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), S4Vectors (>= 0.29.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vegan (>= 2.5-7), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), knitr (>= 1.33), testthat (>= 3.0.2) License: GPL (>= 3) MD5sum: 0b6b2886bb8083f48ca121187e32a906 NeedsCompilation: no Title: Shiny-based interactive data-quality exploration for omics data Description: Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object. biocViews: Visualization, GUI, DimensionReduction, Metabolomics, Proteomics Author: Thomas Naake [aut, cre] (), Wolfgang Huber [aut] () Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: RELEASE_3_15 git_last_commit: 5496301 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MatrixQCvis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixQCvis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixQCvis_1.4.0.tgz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: Shiny-based interactive data quality exploration of omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 166 Package: MatrixRider Version: 1.28.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 MD5sum: 24c8fefd4f7f62de4a3f53c3aad8e842 NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: RELEASE_3_15 git_last_commit: 94bb9bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MatrixRider_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixRider_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixRider_1.28.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 123 Package: matter Version: 1.22.0 Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats, biglm Imports: BiocGenerics, ProtGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 1adb2c0450d6fcdd28aa496015573be3 NeedsCompilation: yes Title: A framework for rapid prototyping with file-based data structures Description: Memory-efficient reading, writing, and manipulation of structured binary data as file-based vectors, matrices, arrays, lists, and data frames. biocViews: Infrastructure, DataRepresentation Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_15 git_last_commit: dac398c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/matter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/matter_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/matter_1.22.0.tgz vignettes: vignettes/matter/inst/doc/matter-supp1.pdf, vignettes/matter/inst/doc/matter-supp2.pdf, vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Supplementary 1 - Simulations and comparative benchmarks, matter: Supplementary 2 - 3D mass spectrometry imaging case study, matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-supp1.R, vignettes/matter/inst/doc/matter-supp2.R, vignettes/matter/inst/doc/matter.R importsMe: Cardinal dependencyCount: 23 Package: MBAmethyl Version: 1.30.0 Depends: R (>= 2.15) License: Artistic-2.0 Archs: x64 MD5sum: 073657447a795f89b7c4c7deee52025a NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: RELEASE_3_15 git_last_commit: 24247a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBAmethyl_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBAmethyl_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBAmethyl_1.30.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.30.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: d690254d0cdbf484e0efb357b2557d40 NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: RELEASE_3_15 git_last_commit: ecb189a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBASED_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBASED_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBASED_1.30.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 35 Package: MBCB Version: 1.50.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 50b14cc143e27eebc52074b2a4ef4c43 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_15 git_last_commit: dcf87e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBCB_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBCB_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBCB_1.50.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: MBECS Version: 1.0.0 Depends: R (>= 4.1) Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap, rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva, tibble, tidyr, vegan, stats, utils, Matrix Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: dcabec23245e6a19c9d6cf56fa12fcb8 NeedsCompilation: no Title: Evaluation and correction of batch effects in microbiome data-sets Description: The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects. biocViews: BatchEffect, Microbiome, ReportWriting, Visualization, Normalization, QualityControl Author: Michael Olbrich [aut, cre] () Maintainer: Michael Olbrich URL: https://github.com/rmolbrich/MBECS VignetteBuilder: knitr BugReports: https://github.com/rmolbrich/MBECS/issues/new git_url: https://git.bioconductor.org/packages/MBECS git_branch: RELEASE_3_15 git_last_commit: 748a3fb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBECS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBECS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBECS_1.0.0.tgz vignettes: vignettes/MBECS/inst/doc/mbecs_vignette.html vignetteTitles: MBECS introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBECS/inst/doc/mbecs_vignette.R dependencyCount: 153 Package: mbkmeans Version: 1.12.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: f6c977e2cf923b5efd8ed437ff2da8f5 NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: RELEASE_3_15 git_last_commit: 3b4cf5e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mbkmeans_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mbkmeans_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mbkmeans_1.12.0.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R dependsOnMe: OSCA.basic importsMe: clusterExperiment suggestsMe: bluster, scDblFinder dependencyCount: 88 Package: mbOmic Version: 1.0.0 Depends: R (>= 4.1.0) Imports: parallel, doParallel, psych, WGCNA, data.table, igraph, visNetwork, cluster, clusterSim, methods, graphics, stats Suggests: testthat (>= 3.0.0), knitr, rmarkdown, devtools, impute License: Artistic-2.0 MD5sum: b03159c908bda1f64d1a4f0e41e388c1 NeedsCompilation: no Title: Integrative analysis of the microbiome and metabolome Description: The mbOmic package contains a set of analysis functions for microbiomics and metabolomics data, designed to analyze the inter-omic correlation between microbiology and metabolites. Integrative analysis of the microbiome and metabolome is the aim of mbOmic. Additionally, the identification of enterotype using the gut microbiota abundance is preliminaryimplemented. biocViews: Metabolomics, Microbiome, Network Author: Congcong Gong [aut, cre] () Maintainer: Congcong Gong URL: https://github.com/gongcongcong/mbOmic VignetteBuilder: knitr BugReports: https://github.com/gongcongcong/mbOmic/issues git_url: https://git.bioconductor.org/packages/mbOmic git_branch: RELEASE_3_15 git_last_commit: a43436f git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/mbOmic_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mbOmic_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mbOmic_1.0.0.tgz vignettes: vignettes/mbOmic/inst/doc/enterotyping.html, vignettes/mbOmic/inst/doc/Integrative_analysis_of_metabolome_and_microbiome.html vignetteTitles: enterotyping, Integrative analysis of metabolome and microbiome hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mbOmic/inst/doc/enterotyping.R, vignettes/mbOmic/inst/doc/Integrative_analysis_of_metabolome_and_microbiome.R dependencyCount: 123 Package: mBPCR Version: 1.50.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) Archs: x64 MD5sum: 9e1c59f951bf4a312228dacf1d3ac3a1 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: RELEASE_3_15 git_last_commit: 8b320af git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mBPCR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mBPCR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mBPCR_1.50.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 100 Package: MBQN Version: 2.8.0 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData, rmarkdown Suggests: knitr License: GPL-3 + file LICENSE MD5sum: 303a3a8aed61c04bbe01010bdcd8aa87 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Eva Brombacher [aut, cre] (), Clemens Kreutz [aut, ctb] (), Ariane Schad [aut, ctb] () Maintainer: Eva Brombacher URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: RELEASE_3_15 git_last_commit: e8ddc7e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBQN_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBQN_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBQN_2.8.0.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 104 Package: MBttest Version: 1.24.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 0fcb6995215cd2bcff05201da88d51a7 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: RELEASE_3_15 git_last_commit: 112e05c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MBttest_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBttest_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBttest_1.24.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: MCbiclust Version: 1.20.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: cfb635ba112410ad74e2a25f43607f12 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: RELEASE_3_15 git_last_commit: 957ec2f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MCbiclust_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MCbiclust_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MCbiclust_1.20.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 133 Package: mCSEA Version: 1.16.0 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 MD5sum: e631afa2c154d64d24ed2530c50826ab NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: RELEASE_3_15 git_last_commit: a472086 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mCSEA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mCSEA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mCSEA_1.16.0.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R suggestsMe: shinyepico dependencyCount: 158 Package: mdp Version: 1.16.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: e1d69cc573111ba2c67ab0c4bd5eeb0b NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: RELEASE_3_15 git_last_commit: 2bbab02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mdp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mdp_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mdp_1.16.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 37 Package: mdqc Version: 1.58.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 9eda5ac5a0b315caf1bc1f8ef45f07ee NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: RELEASE_3_15 git_last_commit: d54a953 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mdqc_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mdqc_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mdqc_1.58.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.16.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 6ab5b090546036af5131c862f56aede3 NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_15 git_last_commit: 3eda634 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MDTS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MDTS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MDTS_1.16.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 44 Package: MEAL Version: 1.26.0 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: bf78b33532cc9835694e0fedd0ec75da NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: RELEASE_3_15 git_last_commit: bac7355 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MEAL_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEAL_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEAL_1.26.0.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 214 Package: MeasurementError.cor Version: 1.68.0 License: LGPL MD5sum: d8a25e44954ec4ca1d65f6ebdb179401 NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: RELEASE_3_15 git_last_commit: 6fcbf20 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MeasurementError.cor_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MeasurementError.cor_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MeasurementError.cor_1.68.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEAT Version: 1.8.0 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 283479b633377ca2d10420a37d04e118 NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues git_url: https://git.bioconductor.org/packages/MEAT git_branch: RELEASE_3_15 git_last_commit: f77426c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MEAT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEAT_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEAT_1.8.0.tgz vignettes: vignettes/MEAT/inst/doc/MEAT.html vignetteTitles: MEAT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAT/inst/doc/MEAT.R dependencyCount: 177 Package: MEB Version: 1.10.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: e63afe88283947c3b1a93a4d12c80ed0 NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data Description: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work. The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. We transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a normalization-invariant minimum enclosing ball (NIMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed NIMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the NIMEB method could be easily extended to different species without normalization. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: RELEASE_3_15 git_last_commit: 71afe86 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MEB_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEB_1.10.0.tgz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 29 Package: MEDIPS Version: 1.48.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: e547e2fa54a128a2bbbdcafc7d74a4b6 NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: RELEASE_3_15 git_last_commit: 609412f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MEDIPS_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEDIPS_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEDIPS_1.48.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R dependencyCount: 103 Package: MEDME Version: 1.56.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: 05bc73d1569dd4ae9837bf94f3593a92 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: RELEASE_3_15 git_last_commit: d4d8a03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MEDME_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEDME_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEDME_1.56.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 108 Package: megadepth Version: 1.6.0 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 MD5sum: 9e8d51db0c4bd69b169a1ad64ba51e58 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (), David Zhang [aut, cre] () Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: RELEASE_3_15 git_last_commit: 8168273 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/megadepth_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/megadepth_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/megadepth_1.6.0.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: dasper, ODER dependencyCount: 84 Package: MEIGOR Version: 1.30.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR, knitr License: GPL-3 Archs: x64 MD5sum: d610383f5419c8ba329d5e47a555900e NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose A. Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: RELEASE_3_15 git_last_commit: 200b726 git_last_commit_date: 2022-04-26 Date/Publication: 2022-06-14 source.ver: src/contrib/MEIGOR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEIGOR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEIGOR_1.30.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R importsMe: bioOED dependencyCount: 76 Package: Melissa Version: 1.12.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 98edb824b64e5c0f7047be159e506e95 NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: RELEASE_3_15 git_last_commit: d2334db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Melissa_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Melissa_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Melissa_1.12.0.tgz vignettes: vignettes/Melissa/inst/doc/process_files.html, vignettes/Melissa/inst/doc/run_melissa.html vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and impute scBS-seq data using Melissa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Melissa/inst/doc/process_files.R, vignettes/Melissa/inst/doc/run_melissa.R dependencyCount: 108 Package: memes Version: 1.4.1 Depends: R (>= 4.1) Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2, ggseqlogo, magrittr, matrixStats, methods, patchwork, processx, purrr, rlang, readr, stats, tools, tibble, tidyr, utils, usethis, universalmotif (>= 1.9.3), xml2 Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0), knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1), rmarkdown, covr License: MIT + file LICENSE MD5sum: 7af98c95b02cab0156dc7739aa6a89da NeedsCompilation: no Title: motif matching, comparison, and de novo discovery using the MEME Suite Description: A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows. biocViews: DataImport, FunctionalGenomics, GeneRegulation, MotifAnnotation, MotifDiscovery, SequenceMatching, Software Author: Spencer Nystrom [aut, cre, cph] () Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: RELEASE_3_15 git_last_commit: affa506 git_last_commit_date: 2022-05-08 Date/Publication: 2022-05-15 source.ver: src/contrib/memes_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/memes_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/memes_1.4.1.tgz vignettes: vignettes/memes/inst/doc/core_ame.html, vignettes/memes/inst/doc/core_dreme.html, vignettes/memes/inst/doc/core_fimo.html, vignettes/memes/inst/doc/core_tomtom.html, vignettes/memes/inst/doc/install_guide.html, vignettes/memes/inst/doc/integrative_analysis.html, vignettes/memes/inst/doc/tidy_motifs.html vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif Discovery Using DREME, Motif Scanning using FIMO, Motif Comparison using TomTom, Install MEME, ChIP-seq Analysis, Tidying Motif Metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/memes/inst/doc/core_ame.R, vignettes/memes/inst/doc/core_dreme.R, vignettes/memes/inst/doc/core_fimo.R, vignettes/memes/inst/doc/core_tomtom.R, vignettes/memes/inst/doc/install_guide.R, vignettes/memes/inst/doc/integrative_analysis.R, vignettes/memes/inst/doc/tidy_motifs.R importsMe: ggmotif dependencyCount: 109 Package: Mergeomics Version: 1.24.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 113a60b11ed8df1a12fe05ec49104bc4 NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: RELEASE_3_15 git_last_commit: a02cfdb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Mergeomics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mergeomics_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mergeomics_1.24.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.32.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: 78601470a830e07d5ead81c4a587ad07 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: RELEASE_3_15 git_last_commit: 599f9c6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MeSHDbi_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MeSHDbi_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MeSHDbi_1.32.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshes, meshr, scTensor dependencyCount: 45 Package: meshes Version: 1.22.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, methods, utils, AnnotationHub, MeSHDbi, yulab.utils Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: b28435514da200e686089b6e3284509e NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: RELEASE_3_15 git_last_commit: 43b51c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/meshes_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/meshes_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/meshes_1.22.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 153 Package: meshr Version: 2.2.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: e8245aecf73d1c5775556ff8872a9183 NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_15 git_last_commit: 7af42d1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/meshr_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/meshr_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/meshr_2.2.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.html vignetteTitles: AnnotationHub-style MeSH ORA Framework from BioC 3.14 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 83 Package: MesKit Version: 1.6.0 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: e558ce94bfd9b3fd9deb9e186f84bfee NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (), Jianyu Chen [aut, ctb] (), Xin Wang [aut, ctb] () Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: RELEASE_3_15 git_last_commit: 2d5b275 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MesKit_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MesKit_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MesKit_1.6.0.tgz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R dependencyCount: 102 Package: messina Version: 1.32.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: 33cb92cc13f010c5be430dc9f7186fee NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: RELEASE_3_15 git_last_commit: a367c57 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/messina_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/messina_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/messina_1.32.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 42 Package: Metab Version: 1.30.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 14c72355a5739dc6edd08e199a6e452e NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/Metab git_branch: RELEASE_3_15 git_last_commit: f4384eb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Metab_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Metab_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Metab_1.30.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R dependencyCount: 96 Package: metabCombiner Version: 1.6.0 Depends: R (>= 4.0), dplyr (>= 1.0) Imports: methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: a6b1b8e885a8be4208f7585e5e111f22 NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: RELEASE_3_15 git_last_commit: af69872 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metabCombiner_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabCombiner_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabCombiner_1.6.0.tgz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 84 Package: MetaboAnnotation Version: 1.0.0 Depends: R (>= 4.0.0) Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics, methods, S4Vectors, Spectra (>= 1.5.7), BiocParallel, SummarizedExperiment, QFeatures, graphics Suggests: testthat, knitr, msdata, BiocStyle, rmarkdown, plotly, shiny, shinyjs, DT License: Artistic-2.0 MD5sum: 720c3bf47dfe69d527d0cf8a1c125279 NeedsCompilation: no Title: Utilities for Annotation of Metabolomics Data Description: High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Michael Witting [aut] (), Johannes Rainer [aut, cre] (), Andrea Vicini [aut] (), Carolin Huber [aut] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboAnnotation VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboAnnotation/issues git_url: https://git.bioconductor.org/packages/MetaboAnnotation git_branch: RELEASE_3_15 git_last_commit: 47f0975 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetaboAnnotation_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboAnnotation_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboAnnotation_1.0.0.tgz vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html vignetteTitles: Annotation of MS-based Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R dependencyCount: 99 Package: MetaboCoreUtils Version: 1.4.0 Depends: R (>= 4.0) Imports: utils, MsCoreUtils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 0967119a35353e020e95924559f6f482 NeedsCompilation: no Title: Core Utils for Metabolomics Data Description: MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Johannes Rainer [aut, cre] (), Michael Witting [aut] (), Andrea Vicini [aut], Liesa Salzer [ctb] (), Sebastian Gibb [ctb] (), Michael Stravs [ctb] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues git_url: https://git.bioconductor.org/packages/MetaboCoreUtils git_branch: RELEASE_3_15 git_last_commit: 594555c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetaboCoreUtils_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboCoreUtils_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboCoreUtils_1.4.0.tgz vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html vignetteTitles: Core Utils for Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, MobilityTransformR dependencyCount: 13 Package: metabolomicsWorkbenchR Version: 1.6.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 MD5sum: 3efae1eaa070ce7d7046c0ad78c72ede NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: RELEASE_3_15 git_last_commit: 6f3ba94 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metabolomicsWorkbenchR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabolomicsWorkbenchR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabolomicsWorkbenchR_1.6.0.tgz vignettes: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html vignetteTitles: Example using structToolbox, Introduction_to_metabolomicsWorkbenchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R suggestsMe: fobitools dependencyCount: 69 Package: metabomxtr Version: 1.30.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: b4d82146a39005d7391c0c144ef03465 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: RELEASE_3_15 git_last_commit: 0338653 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metabomxtr_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabomxtr_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabomxtr_1.30.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 55 Package: MetaboSignal Version: 1.26.2 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: a2f21d7ba3d80e517545961cee0dcd70 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: RELEASE_3_15 git_last_commit: 54eefda git_last_commit_date: 2022-07-27 Date/Publication: 2022-07-28 source.ver: src/contrib/MetaboSignal_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboSignal_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboSignal_1.26.2.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 198 Package: metaCCA Version: 1.24.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 1fbf8aef86fd11603f835f179c4e9273 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: RELEASE_3_15 git_last_commit: aed3851 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metaCCA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaCCA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaCCA_1.24.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.18.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr, rmarkdown License: GPL (>= 2) MD5sum: a526ff5c12a03e74dd5f53844850d28c NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_15 git_last_commit: 81aa622 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetaCyto_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/MetaCyto_1.18.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 196 Package: metagene Version: 2.28.1 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, Rsamtools, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: d252fd04760bc73e28c32e33e1cd11f0 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_15 git_last_commit: c3ea51b git_last_commit_date: 2022-09-13 Date/Publication: 2022-09-15 source.ver: src/contrib/metagene_2.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagene_2.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/metagene_2.28.1.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependencyCount: 121 Package: metagene2 Version: 1.12.0 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 0610c1042f0cada18e8408db22c5abe1 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: RELEASE_3_15 git_last_commit: 9345b06 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metagene2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagene2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metagene2_1.12.0.tgz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 83 Package: metagenomeSeq Version: 1.38.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW License: Artistic-2.0 MD5sum: 95884ec12501b941ce1719f5aa387b0a NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_15 git_last_commit: 50be9db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metagenomeSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagenomeSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metagenomeSeq_1.38.0.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: metavizr, microbiomeExplorer, etec16s importsMe: benchdamic, Maaslin2, microbiomeDASim, microbiomeMarker, MetaLonDA suggestsMe: interactiveDisplay, phyloseq, scTreeViz, Wrench dependencyCount: 30 Package: metahdep Version: 1.54.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: 5a6a1fa83231fe83d11ffff0c5cb3f77 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: RELEASE_3_15 git_last_commit: 8ab4074 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metahdep_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metahdep_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metahdep_1.54.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.32.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 8cbec34ed470f65f77551acbdd634fb6 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: RELEASE_3_15 git_last_commit: ff2821c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metaMS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaMS_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaMS_1.32.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 127 Package: MetaNeighbor Version: 1.16.0 Depends: R(>= 3.5) Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4), Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1.2), SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph, dplyr, tidyr, tibble, ggplot2 Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2), UpSetR License: MIT + file LICENSE Archs: x64 MD5sum: 67be877fe1a5d36bdc4636f404892433 NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Stephan Fischer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: RELEASE_3_15 git_last_commit: b8e6c01 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetaNeighbor_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaNeighbor_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaNeighbor_1.16.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 67 Package: metapod Version: 1.4.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 0d4530fa573603a55f02b3e7bf9991a3 NeedsCompilation: yes Title: Meta-Analyses on P-Values of Differential Analyses Description: Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. biocViews: MultipleComparison, DifferentialPeakCalling Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapod git_branch: RELEASE_3_15 git_last_commit: e71c207 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metapod_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metapod_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metapod_1.4.0.tgz vignettes: vignettes/metapod/inst/doc/overview.html vignetteTitles: Meta-analysis strategies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapod/inst/doc/overview.R importsMe: csaw, mumosa, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.2.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 02f7e0b82b67d3ad035f6dcf093fc2bd NeedsCompilation: no Title: Conducts pathway test of metabolomics data using a weighted permutation test Description: The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. biocViews: Technology, MassSpectrometry, Metabolomics, Pathways Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: RELEASE_3_15 git_last_commit: 6164c92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metapone_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metapone_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metapone_1.2.0.tgz vignettes: vignettes/metapone/inst/doc/metapone.html vignetteTitles: metapone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapone/inst/doc/metapone.R dependencyCount: 61 Package: metaSeq Version: 1.36.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 Archs: x64 MD5sum: 067314b52da5478281cac8b970035a85 NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: RELEASE_3_15 git_last_commit: 086aaae git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metaSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaSeq_1.36.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 14 Package: metaseqR2 Version: 1.8.2 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, baySeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) MD5sum: b45b3afb791eca2b289359b6e258a3fc NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues git_url: https://git.bioconductor.org/packages/metaseqR2 git_branch: RELEASE_3_15 git_last_commit: 2576302 git_last_commit_date: 2022-07-08 Date/Publication: 2022-07-12 source.ver: src/contrib/metaseqR2_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaseqR2_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/metaseqR2_1.8.2.tgz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 228 Package: metavizr Version: 1.20.0 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, ExperimentHub, tidyr, rmarkdown License: MIT + file LICENSE MD5sum: 14b2323903d719f56657dd0cccba0009 NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics, ImmunoOncology Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metavizr git_branch: RELEASE_3_15 git_last_commit: 596a30e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/metavizr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metavizr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metavizr_1.20.0.tgz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R dependencyCount: 161 Package: MetaVolcanoR Version: 1.10.0 Depends: R (>= 4.1.1) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, markdown, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 530f8b9ff206240772b710a62d8cb213 NeedsCompilation: no Title: Gene Expression Meta-analysis Visualization Tool Description: MetaVolcanoR combines differential gene expression results. It implements three strategies to summarize differential gene expression from different studies. i) Random Effects Model (REM) approach, ii) a p-value combining-approach, and iii) a vote-counting approach. In all cases, MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, mRNAMicroarray, RNASeq Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya [aut, ths] Maintainer: Cesar Prada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaVolcanoR git_branch: RELEASE_3_15 git_last_commit: e19ff15 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetaVolcanoR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaVolcanoR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaVolcanoR_1.10.0.tgz vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html vignetteTitles: MetaVolcanoR: Differential expression meta-analysis tool, MetaVolcanoR inputs: differential expression examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R dependencyCount: 97 Package: MetCirc Version: 1.26.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), MSnbase (>= 2.15.3), Imports: ggplot2 (>= 3.2.1), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), methods (>= 3.5), stats (>= 3.5), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 10bec3e46971b983290a9a942fedaa52 NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectrum2 and MSpectra infrastructure defined in the package MSnbase that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: RELEASE_3_15 git_last_commit: 54d04ee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetCirc_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetCirc_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetCirc_1.26.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 102 Package: methimpute Version: 1.18.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 9fc881a2156cfe67257ed38e29a04b03 NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: RELEASE_3_15 git_last_commit: eb32b6c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methimpute_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methimpute_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methimpute_1.18.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 57 Package: methInheritSim Version: 1.18.0 Depends: R (>= 3.5.0) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: d6a854dd2c3979a83058624a4f55c0b4 NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: RELEASE_3_15 git_last_commit: 9363be5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methInheritSim_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methInheritSim_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methInheritSim_1.18.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 98 Package: MethPed Version: 1.24.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 Archs: x64 MD5sum: bedce81edea8c80b6cd0a8bfd05d94a0 NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: RELEASE_3_15 git_last_commit: de447cc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethPed_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethPed_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethPed_1.24.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 8 Package: MethReg Version: 1.6.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, sesame, AnnotationHub, ExperimentHub, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress, utils Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, downloader, R.utils, doParallel, reshape2, JASPAR2020, TFBSTools, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 545e319acb2aaea06c5d2d269fd7e53f NeedsCompilation: no Title: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription Description: Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis. biocViews: MethylationArray, Regression, GeneExpression, Epigenetics, GeneTarget, Transcription Author: Tiago Silva [aut, cre] (), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: RELEASE_3_15 git_last_commit: 41583fc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethReg_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethReg_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethReg_1.6.0.tgz vignettes: vignettes/MethReg/inst/doc/MethReg.html vignetteTitles: MethReg: estimating regulatory potential of DNA methylation in gene transcription hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethReg/inst/doc/MethReg.R dependencyCount: 185 Package: methrix Version: 1.10.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 393caa68e7ce1c80b0457eef9f480eec NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (), Reka Toth [aut] (), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues git_url: https://git.bioconductor.org/packages/methrix git_branch: RELEASE_3_15 git_last_commit: 321ca92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methrix_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methrix_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methrix_1.10.0.tgz vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R dependencyCount: 82 Package: MethTargetedNGS Version: 1.28.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 0f573238b0effc8703441758cfb3a485 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: RELEASE_3_15 git_last_commit: f12d3cf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethTargetedNGS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethTargetedNGS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethTargetedNGS_1.28.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 35 Package: MethylAid Version: 1.30.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 8b31c8bd16733bee908b9b99bcff267d NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: RELEASE_3_15 git_last_commit: 0d3c069 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethylAid_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylAid_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylAid_1.30.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependsOnMe: MethylAidData dependencyCount: 167 Package: methylCC Version: 1.10.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: CC BY 4.0 MD5sum: 7b17a86924421298ca249f7532a9384f NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: RELEASE_3_15 git_last_commit: cf69213 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylCC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylCC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylCC_1.10.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The methylCC user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 157 Package: methylclock Version: 1.2.1 Depends: R (>= 4.1.0), methylclockData, devtools, quadprog Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute, PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2, ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra, preprocessCore, dynamicTreeCut, planet LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE MD5sum: 4df9eb72fe445ff758e88537307a305f NeedsCompilation: yes Title: Methylclock - DNA methylation-based clocks Description: This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (), Juan R. Gonzalez [aut] () Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/methylclock VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/methylclock/issues git_url: https://git.bioconductor.org/packages/methylclock git_branch: RELEASE_3_15 git_last_commit: 7214c6b git_last_commit_date: 2022-07-11 Date/Publication: 2022-07-12 source.ver: src/contrib/methylclock_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylclock_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/methylclock_1.2.1.tgz vignettes: vignettes/methylclock/inst/doc/methylclock.html vignetteTitles: DNAm age using diffrent methylation clocks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methylclock/inst/doc/methylclock.R dependencyCount: 287 Package: methylGSA Version: 1.14.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 MD5sum: f207622cc9659a8130a5eaa4b4ca24fd NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: RELEASE_3_15 git_last_commit: 9f2c1d1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylGSA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylGSA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylGSA_1.14.0.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 216 Package: methylInheritance Version: 1.20.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 Archs: x64 MD5sum: a8b38b583fe6af051b26b1b11af8e8c7 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes [cre, aut] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: RELEASE_3_15 git_last_commit: 8a86ab8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylInheritance_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylInheritance_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylInheritance_1.20.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 101 Package: methylKit Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: b5db073e3f88023715c9056edf8260ed NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_15 git_last_commit: 29b567d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylKit_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylKit_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylKit_1.22.0.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: deconvR, methInheritSim, methylInheritance dependencyCount: 94 Package: MethylMix Version: 2.26.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: da70dc7faef408c1016d9f8a04100b70 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_15 git_last_commit: ed5443f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethylMix_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylMix_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylMix_2.26.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 51 Package: methylMnM Version: 1.34.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: x64 MD5sum: fad73a9d2405ed0adf441ac561fd642a NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: RELEASE_3_15 git_last_commit: 5981df0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylMnM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylMnM_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylMnM_1.34.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 12 Package: methylPipe Version: 1.30.0 Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) MD5sum: 4ede0bcbe8a42a4438e4acd9d063fa47 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: RELEASE_3_15 git_last_commit: 2a0b33e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylPipe_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylPipe_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylPipe_1.30.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 152 Package: methylscaper Version: 1.4.0 Depends: R (>= 4.1.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: knitr, rmarkdown, devtools License: GPL-2 Archs: x64 MD5sum: b4e28dafeaab2b201714f733d1dd01fd NeedsCompilation: no Title: Visualization of Methylation Data Description: methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. biocViews: DNAMethylation, Epigenetics, PrincipalComponent, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylscaper git_branch: RELEASE_3_15 git_last_commit: 5438798 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylscaper_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylscaper_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylscaper_1.4.0.tgz vignettes: vignettes/methylscaper/inst/doc/methylScaper.html vignetteTitles: Using methylscaper to visualize joint methylation and nucleosome occupancy data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R dependencyCount: 95 Package: MethylSeekR Version: 1.36.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) Archs: x64 MD5sum: d4ad2fa951cdc37dc6ed96f2f033cd9a NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: RELEASE_3_15 git_last_commit: 6024af5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MethylSeekR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylSeekR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylSeekR_1.36.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 78 Package: methylSig Version: 1.8.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, GenomeInfoDb, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 MD5sum: 5f87900ed7e90bb8aa9b44cc9ba1e421 NeedsCompilation: no Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: RELEASE_3_15 git_last_commit: be59631 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylSig_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylSig_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylSig_1.8.0.tgz vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html, vignettes/methylSig/inst/doc/using-methylSig.html vignetteTitles: Updating methylSig code, Using methylSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R, vignettes/methylSig/inst/doc/using-methylSig.R dependencyCount: 77 Package: methylumi Version: 2.42.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio, GenomicFeatures Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: ff87a7143e87a78a861a1fab3dea86b2 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: RELEASE_3_15 git_last_commit: 73e9c7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/methylumi_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylumi_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylumi_2.42.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, missMethyl dependencyCount: 156 Package: MetID Version: 1.14.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: 802b4e05457b5d86999ed1cdd0216ee6 NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: RELEASE_3_15 git_last_commit: ab83e82 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetID_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetID_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetID_1.14.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 132 Package: MetNet Version: 1.14.0 Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3 (>= 1.7.0), methods (>= 3.5), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>= 1.6.0) License: GPL (>= 3) MD5sum: 4720414123ef3be80d0804d941c5f314 NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre], Liesa Salzer [ctb] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: RELEASE_3_15 git_last_commit: 3f02700 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MetNet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetNet_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetNet_1.14.0.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 84 Package: mfa Version: 1.18.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 3a83718a6fb364461616b438d6ffbb0f NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: RELEASE_3_15 git_last_commit: 77a1886 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mfa_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mfa_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mfa_1.18.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter dependencyCount: 69 Package: Mfuzz Version: 2.56.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 271ea8fff9656681b29e3b74c1da7e58 NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: RELEASE_3_15 git_last_commit: fe52806 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Mfuzz_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mfuzz_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mfuzz_2.56.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, TimiRGeN importsMe: Patterns suggestsMe: DAPAR, pwOmics dependencyCount: 16 Package: MGFM Version: 1.30.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 Archs: x64 MD5sum: 7049ad8a4836dad4a2d4703f742452a2 NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: RELEASE_3_15 git_last_commit: c75dc77 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MGFM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MGFM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MGFM_1.30.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 48 Package: MGFR Version: 1.22.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 MD5sum: 9960cf8295f6863633083bc3e7712c9e NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: RELEASE_3_15 git_last_commit: 6b3d17a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MGFR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MGFR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MGFR_1.22.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier dependencyCount: 73 Package: mgsa Version: 1.44.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: 9eda19fa1b822baa93270373e28fbafc NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: RELEASE_3_15 git_last_commit: 37049b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mgsa_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mgsa_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mgsa_1.44.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R suggestsMe: pareg dependencyCount: 9 Package: mia Version: 1.4.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, TreeSummarizedExperiment (>= 1.99.3), MultiAssayExperiment Imports: methods, stats, utils, MASS, ape, decontam, vegan, BiocGenerics, S4Vectors, IRanges, Biostrings, DECIPHER, BiocParallel, DelayedArray, DelayedMatrixStats, scuttle, scater, DirichletMultinomial, rlang, dplyr, tibble, tidyr Suggests: testthat, knitr, patchwork, BiocStyle, yaml, phyloseq, dada2, stringr, biomformat, reldist, ade4, microbiomeDataSets, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 2884e338c3afd7c99bc0fe80364dd626 NeedsCompilation: no Title: Microbiome analysis Description: mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. biocViews: Microbiome, Software, DataImport Author: Felix G.M. Ernst [aut, cre] (), Sudarshan A. Shetty [aut] (), Tuomas Borman [aut] (), Leo Lahti [aut] (), Yang Cao [ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos [ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb], Domenick Braccia [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/microbiome/mia VignetteBuilder: knitr BugReports: https://github.com/microbiome/mia/issues git_url: https://git.bioconductor.org/packages/mia git_branch: RELEASE_3_15 git_last_commit: 18fd690 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mia_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mia_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mia_1.4.0.tgz vignettes: vignettes/mia/inst/doc/mia.html vignetteTitles: mia hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mia/inst/doc/mia.R dependsOnMe: miaViz importsMe: ANCOMBC, curatedMetagenomicData suggestsMe: CBEA, philr dependencyCount: 120 Package: miaSim Version: 1.2.0 Depends: SummarizedExperiment, TreeSummarizedExperiment Imports: deSolve, stats, poweRlaw, gtools, S4Vectors, MatrixGenerics Suggests: rmarkdown, knitr, BiocStyle, testthat License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: bef0e94833e500d02a66672a8801dd9f NeedsCompilation: no Title: Microbiome Data Simulation Description: Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to SummarizedExperiment or TreeSummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Yagmur Simsek [cre, aut], Leo Lahti [aut] () Maintainer: Yagmur Simsek URL: https://github.com/microbiome/miaSim VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaSim/issues git_url: https://git.bioconductor.org/packages/miaSim git_branch: RELEASE_3_15 git_last_commit: 496a898 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/miaSim_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miaSim_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miaSim_1.2.0.tgz vignettes: vignettes/miaSim/inst/doc/miaSim.html vignetteTitles: miaSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaSim/inst/doc/miaSim.R dependencyCount: 70 Package: miaViz Version: 1.4.0 Depends: R (>= 4.0), SummarizedExperiment, TreeSummarizedExperiment, mia (>= 0.99), ggplot2, ggraph (>= 2.0) Imports: methods, stats, S4Vectors, BiocGenerics, BiocParallel, DelayedArray, scater, ggtree, ggnewscale, viridis, tibble, tidytree, tidygraph, rlang, purrr, tidyr, dplyr, ape, DirichletMultinomial Suggests: knitr, rmarkdown, BiocStyle, testthat, patchwork, microbiomeDataSets License: Artistic-2.0 | file LICENSE MD5sum: 09ea866533932674bc9fa06c059e93f4 NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements plotting function to work with TreeSummarizedExperiment and related objects in a context of microbiome analysis. Among others this includes plotting tree, graph and microbiome series data. The package is part of the broader miaverse framework. biocViews: Microbiome, Software, Visualization Author: Felix G.M. Ernst [aut, cre] (), Tuomas Borman [aut] (), Leo Lahti [aut] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: RELEASE_3_15 git_last_commit: 309f516 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miaViz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miaViz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miaViz_1.4.0.tgz vignettes: vignettes/miaViz/inst/doc/miaViz.html vignetteTitles: miaViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaViz/inst/doc/miaViz.R dependencyCount: 137 Package: MiChip Version: 1.50.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 702f1a6ac86b88b9a9c0e1936d751fec NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: RELEASE_3_15 git_last_commit: 8fefb13 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MiChip_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiChip_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiChip_1.50.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 6 Package: microbiome Version: 1.18.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: 5eb6b4b9ba283f893892b7abf00c703c NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (), Sudarshan Shetty [aut] () Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: RELEASE_3_15 git_last_commit: 4887f83 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/microbiome_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiome_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiome_1.18.0.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R suggestsMe: ANCOMBC dependencyCount: 90 Package: microbiomeDASim Version: 1.10.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE MD5sum: 502659f0abe7b740f1bf428c27b8d7ef NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: RELEASE_3_15 git_last_commit: a691af7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/microbiomeDASim_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeDASim_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeDASim_1.10.0.tgz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 94 Package: microbiomeExplorer Version: 1.6.1 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: c3ad772006e2598ca259eb5f832c5d43 NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: RELEASE_3_15 git_last_commit: 17f323d git_last_commit_date: 2022-09-01 Date/Publication: 2022-09-04 source.ver: src/contrib/microbiomeExplorer_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeExplorer_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeExplorer_1.6.1.tgz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 207 Package: microbiomeMarker Version: 1.2.2 Depends: R (>= 4.1.0) Imports: dplyr, phyloseq, magrittr, purrr, MASS, utils, ggplot2, tibble, rlang, stats, coin, ggtree, tidytree, methods, IRanges, tidyr, patchwork, ggsignif, metagenomeSeq, DESeq2, edgeR, BiocGenerics, Biostrings, yaml, biomformat, S4Vectors, Biobase, ComplexHeatmap, ANCOMBC, caret, limma, ALDEx2, multtest, plotROC, vegan, pROC, BiocParallel Suggests: testthat, covr, glmnet, Matrix, kernlab, e1071, ranger, knitr, rmarkdown, BiocStyle, withr License: GPL-3 MD5sum: 26fa0e67fc8484efe5a118ec990526bf NeedsCompilation: no Title: microbiome biomarker analysis toolkit Description: To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers. biocViews: Metagenomics, Microbiome, DifferentialExpression Author: Yang Cao [aut, cre] Maintainer: Yang Cao URL: https://github.com/yiluheihei/microbiomeMarker VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/microbiomeMarker/issues git_url: https://git.bioconductor.org/packages/microbiomeMarker git_branch: RELEASE_3_15 git_last_commit: a51c6f2 git_last_commit_date: 2022-06-15 Date/Publication: 2022-06-16 source.ver: src/contrib/microbiomeMarker_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeMarker_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeMarker_1.2.2.tgz vignettes: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.html vignetteTitles: Tools for microbiome marker identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.R dependencyCount: 293 Package: MicrobiomeProfiler Version: 1.2.0 Depends: R (>= 4.1.0) Imports: clusterProfiler (>= 4.0.2), config, DT, enrichplot, golem, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-2 MD5sum: 8fd3d3eeda46d4c0462603167dc91637 NeedsCompilation: no Title: An R/shiny package for microbiome functional enrichment analysis Description: This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis. biocViews: Microbiome, Software, Visualization,KEGG Author: Meijun Chen [aut, cre] (), Guangchuang Yu [aut, ths] () Maintainer: Meijun Chen URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler git_branch: RELEASE_3_15 git_last_commit: fa84365 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MicrobiomeProfiler_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MicrobiomeProfiler_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MicrobiomeProfiler_1.2.0.tgz vignettes: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html vignetteTitles: MicrobiomeProfiler hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R dependencyCount: 173 Package: MicrobiotaProcess Version: 1.8.2 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.3.9), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, plyr, dtplyr, ggtreeExtra Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, jsonlite, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, corrr, ggupset, ggVennDiagram, gghalves, ggalluvial (>= 0.11.1), forcats, cli, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach License: GPL (>= 3.0) MD5sum: ba556a18893e6ad790003dd58d330e22 NeedsCompilation: no Title: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: RELEASE_3_15 git_last_commit: 48b0eec git_last_commit_date: 2022-09-18 Date/Publication: 2022-09-20 source.ver: src/contrib/MicrobiotaProcess_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/MicrobiotaProcess_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/MicrobiotaProcess_1.8.2.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R dependencyCount: 100 Package: microRNA Version: 1.54.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: c64434de25c4e5eed54bca8b7d1e9d4e NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: RELEASE_3_15 git_last_commit: c50a4e9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/microRNA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microRNA_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microRNA_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 18 Package: midasHLA Version: 1.4.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3) Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1), formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra (>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods, stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1), stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1), utils, qdapTools (>= 1.3.3) Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>= 2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown, seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1), tidyr (>= 1.1.2) License: MIT + file LICENCE MD5sum: c2f168d1c5e2b3a72582b2415380be63 NeedsCompilation: no Title: R package for immunogenomics data handling and association analysis Description: MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology. biocViews: CellBiology, Genetics, StatisticalMethod Author: Christian Hammer [aut], Maciej Migdał [aut, cre] Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: RELEASE_3_15 git_last_commit: 2351949 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/midasHLA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/midasHLA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/midasHLA_1.4.0.tgz vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html, vignettes/midasHLA/inst/doc/MiDAS_vignette.html vignetteTitles: MiDAS tutorial, MiDAS quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R, vignettes/midasHLA/inst/doc/MiDAS_vignette.R dependencyCount: 114 Package: MIGSA Version: 1.20.0 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, jsonlite, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, stats, utils, vegan Suggests: BiocStyle, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, knitr, mGSZ, MIGSAdata, RUnit License: GPL (>= 2) Archs: x64 MD5sum: dc74db336e13e0dfd883b4a31fbf2690 NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: https://github.com/jcrodriguez1989/MIGSA/ BugReports: https://github.com/jcrodriguez1989/MIGSA/issues git_url: https://git.bioconductor.org/packages/MIGSA git_branch: RELEASE_3_15 git_last_commit: e0e8af0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MIGSA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIGSA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIGSA_1.20.0.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R dependencyCount: 108 Package: miloR Version: 1.4.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), S4Vectors, stats, stringr, methods, igraph, irlba, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices Suggests: testthat, MASS, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, curl, graphics License: GPL-3 + file LICENSE MD5sum: 96aa431befee2cc034d82aa4b99352fe NeedsCompilation: no Title: Differential neighbourhood abundance testing on a graph Description: Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model. biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software Author: Mike Morgan [aut, cre], Emma Dann [aut, ctb] Maintainer: Mike Morgan URL: https://marionilab.github.io/miloR VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/miloR/issues git_url: https://git.bioconductor.org/packages/miloR git_branch: RELEASE_3_15 git_last_commit: d48e182 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miloR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miloR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miloR_1.4.0.tgz vignettes: vignettes/miloR/inst/doc/milo_contrasts.html, vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html vignetteTitles: Using contrasts for differential abundance testing, Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R, vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R dependencyCount: 102 Package: mimager Version: 1.20.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: 56eaf25fd1412d3c987e56e4b2d44fe4 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: RELEASE_3_15 git_last_commit: 07504d7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mimager_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mimager_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mimager_1.20.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 66 Package: MIMOSA Version: 1.34.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, dplyr, tidyr, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: MIT + file LICENSE MD5sum: e18c69e731ad9302e81dd5f22523f206 NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MIMOSA git_branch: RELEASE_3_15 git_last_commit: 20f68ef git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MIMOSA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIMOSA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIMOSA_1.34.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R dependencyCount: 89 Package: mina Version: 1.4.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL MD5sum: a4f0b0337fcb400a8a328af723a54c14 NeedsCompilation: yes Title: Microbial community dIversity and Network Analysis Description: An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way. biocViews: Software, WorkflowStep Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb] Maintainer: Rui Guan VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: RELEASE_3_15 git_last_commit: 19780c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mina_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mina_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mina_1.4.0.tgz vignettes: vignettes/mina/inst/doc/mina.html vignetteTitles: Microbial dIversity and Network Analysis with MINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mina/inst/doc/mina.R dependencyCount: 90 Package: MineICA Version: 1.36.1 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX, future, future.apply Enhances: doMC License: GPL-2 Archs: x64 MD5sum: ae7e5f48f09101a848180087ab7e7245 NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: RELEASE_3_15 git_last_commit: a24c22f git_last_commit_date: 2022-09-07 Date/Publication: 2022-09-08 source.ver: src/contrib/MineICA_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MineICA_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MineICA_1.36.1.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 211 Package: minet Version: 3.54.0 Imports: infotheo License: Artistic-2.0 Archs: x64 MD5sum: fefe597d44660d0327de61413364e496 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: RELEASE_3_15 git_last_commit: eb34f68 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/minet_3.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/minet_3.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/minet_3.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, netOmics, RTN, TCGAWorkflow, TGS suggestsMe: CNORfeeder, TCGAbiolinks, dnapath, WGCNA dependencyCount: 1 Package: minfi Version: 1.42.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 MD5sum: a8cbde7d07f2e7bd09238dee4292add6 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_15 git_last_commit: 30fc705 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/minfi_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/minfi_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/minfi_1.42.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, conumee, methylumi, REMP, shinyMethyl, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, BeadSorted.Saliva.EPIC, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, minfiData, minfiDataEPIC, methylationArrayAnalysis importsMe: DMRcate, epimutacions, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl, quantro, recountmethylation, shinyepico, skewr, EMAS suggestsMe: EnMCB, epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, planet, RnBeads, brgedata, epimutacionsData, GSE159526, GeoTcgaData, MLML2R dependencyCount: 141 Package: MinimumDistance Version: 1.40.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 Archs: x64 MD5sum: 02ac929af3c09b5d42b637d1b6195cf4 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: RELEASE_3_15 git_last_commit: 15db0ee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MinimumDistance_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MinimumDistance_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MinimumDistance_1.40.0.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 85 Package: MiPP Version: 1.68.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 75145f171b1d796c91a0ad7519ea4fb4 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: RELEASE_3_15 git_last_commit: 31a2716 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MiPP_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiPP_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiPP_1.68.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: miQC Version: 1.4.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, biomaRt, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 0bbc760dd9487230539a335eb5943a3d NeedsCompilation: no Title: Flexible, probabilistic metrics for quality control of scRNA-seq data Description: Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing, Sequencing Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut] Maintainer: Ariel Hippen URL: https://github.com/greenelab/miQC VignetteBuilder: knitr BugReports: https://github.com/greenelab/miQC/issues git_url: https://git.bioconductor.org/packages/miQC git_branch: RELEASE_3_15 git_last_commit: cbe88ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miQC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miQC_1.4.0.tgz vignettes: vignettes/miQC/inst/doc/miQC.html vignetteTitles: miQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miQC/inst/doc/miQC.R dependencyCount: 57 Package: MIRA Version: 1.18.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 4273c0343621806b7ac8f1bd6f79ac84 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: RELEASE_3_15 git_last_commit: 297ceb8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MIRA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIRA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIRA_1.18.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 91 Package: MiRaGE Version: 1.38.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: ef8626cfdbf2a965bad76588e96fbc2b NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: RELEASE_3_15 git_last_commit: dbdb6c4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MiRaGE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiRaGE_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiRaGE_1.38.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 46 Package: miRBaseConverter Version: 1.20.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 591ee59cf80fe7479ae8eb44830359c6 NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_15 git_last_commit: 94d9300 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRBaseConverter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRBaseConverter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRBaseConverter_1.20.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R importsMe: SPONGE, ExpHunterSuite dependencyCount: 1 Package: miRcomp Version: 1.26.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: 93ebf06ca70fd9a095796687dd40b3bd NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: RELEASE_3_15 git_last_commit: 6fff916 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRcomp_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRcomp_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRcomp_1.26.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 8 Package: mirIntegrator Version: 1.26.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: ee12746d9c08ae733edeaf25455b39ba NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: RELEASE_3_15 git_last_commit: 2392861 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mirIntegrator_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mirIntegrator_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mirIntegrator_1.26.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 77 Package: miRLAB Version: 1.26.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown License: GPL (>=2) MD5sum: efb6a955057e8e6123e8554d4717f734 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_15 git_last_commit: 8276c9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRLAB_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRLAB_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRLAB_1.26.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 190 Package: miRmine Version: 1.18.0 Depends: R (>= 3.5.0), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: af7746a8eaae3e1b8a1ae1b9eee96cc0 NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRmine git_branch: RELEASE_3_15 git_last_commit: a03585d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRmine_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRmine_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRmine_1.18.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R dependencyCount: 25 Package: miRNAmeConverter Version: 1.24.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: dfd915c6afdf87f77d710be839f0caf1 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: RELEASE_3_15 git_last_commit: bbfed09 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRNAmeConverter_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNAmeConverter_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNAmeConverter_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 52 Package: miRNApath Version: 1.56.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 615a1ff97dcc274772631ec6e26e98da NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: RELEASE_3_15 git_last_commit: d69c7fd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRNApath_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNApath_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNApath_1.56.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.30.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 034aa57949c70d42a4db6d75611bcf89 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: RELEASE_3_15 git_last_commit: 4feacc9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRNAtap_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNAtap_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNAtap_1.30.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: SpidermiR, miRNAtap.db dependencyCount: 53 Package: miRSM Version: 1.14.0 Depends: R (>= 3.5.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, NMF, biclust, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: f49e4c1c67a77b7f01148e036248c21e NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge modules in heterogeneous data. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: RELEASE_3_15 git_last_commit: e5f5b4c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRSM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRSM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRSM_1.14.0.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 354 Package: miRspongeR Version: 2.0.0 Depends: R (>= 3.5.0) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, linkcomm, utils, Rcpp, org.Hs.eg.db, SPONGE, foreach, doParallel Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: c6dce0f7dbd8145455a153d484b7cf49 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge regulation Description: This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software, SingleCell, Spatial, RNASeq Author: Junpeng Zhang Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: RELEASE_3_15 git_last_commit: 72a3c38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/miRspongeR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRspongeR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRspongeR_2.0.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: Identification and analysis of miRNA sponge regulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 278 Package: mirTarRnaSeq Version: 1.4.0 Depends: R (>= 4.1.0), ggplot2 Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE License: MIT + file LICENSE Archs: x64 MD5sum: 4006d813211710957b6e3075b2d7a60a NeedsCompilation: no Title: mirTarRnaSeq Description: mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments. biocViews: miRNA, Regression, Software, Sequencing, SmallRNA, TimeCourse, DifferentialExpression Author: Mercedeh Movassagh [aut, cre] (), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: RELEASE_3_15 git_last_commit: 48cc1ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mirTarRnaSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mirTarRnaSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mirTarRnaSeq_1.4.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 57 Package: missMethyl Version: 1.30.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 Archs: x64 MD5sum: 759a771b7697a1987f54fbadd5f4406b NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: RELEASE_3_15 git_last_commit: 7348466 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/missMethyl_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/missMethyl_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/missMethyl_1.30.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 166 Package: missRows Version: 1.16.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 95c5c8267e6751322b41c1563b7bf7d7 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: RELEASE_3_15 git_last_commit: a99a18a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/missRows_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/missRows_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/missRows_1.16.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 64 Package: mistyR Version: 1.4.0 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr, filelock, furrr (>= 0.2.0), ggplot2, methods, purrr, ranger, readr, ridge, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, utils, withr Suggests: BiocStyle, covr, future, igraph (>= 1.2.7), knitr, MASS, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 9055ec8ed8665ef930d25d48829790c7 NeedsCompilation: no Title: Multiview Intercellular SpaTial modeling framework Description: mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution. biocViews: Software, BiomedicalInformatics, CellBiology, SystemsBiology, Regression, DecisionTree, SingleCell, Spatial Author: Jovan Tanevski [cre, aut] (), Ricardo Omar Ramirez Flores [ctb] (), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski URL: https://saezlab.github.io/mistyR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: RELEASE_3_15 git_last_commit: f75bba8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mistyR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mistyR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mistyR_1.4.0.tgz vignettes: vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R dependencyCount: 106 Package: mitch Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r Suggests: stringi, testthat (>= 2.1.0) License: CC BY-SA 4.0 + file LICENSE MD5sum: bb63816d07abbb3738c6a529c59095cf NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome Author: Mark Ziemann [aut, cre, cph], Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitch git_branch: RELEASE_3_15 git_last_commit: d958acf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mitch_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mitch_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mitch_1.8.0.tgz vignettes: vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 98 Package: mitoClone2 Version: 1.2.0 Depends: R (>= 4.1.0) Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices, graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods, ggplot2 LinkingTo: Rhtslib (>= 1.13.1) Suggests: knitr, rmarkdown, Biostrings, testthat License: GPL-3 MD5sum: 7313f2c57b9e774a98421e34035e7938 NeedsCompilation: yes Title: Clonal Population Identification in Single-Cell RNA-Seq Data using Mitochondrial and Somatic Mutations Description: This package primarily identifies variants in mitochondrial genomes from BAM alignment files. It filters these variants to remove RNA editing events then estimates their evolutionary relationship (i.e. their phylogenetic tree) and groups single cells into clones. It also visualizes the mutations and providing additional genomic context. biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell, Alignment Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke [aut] Maintainer: Benjamin Story URL: https://github.com/benstory/mitoClone2 SystemRequirements: GNU make, PhISCS (optional) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitoClone2 git_branch: RELEASE_3_15 git_last_commit: f1bad29 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mitoClone2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mitoClone2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mitoClone2_1.2.0.tgz vignettes: vignettes/mitoClone2/inst/doc/clustering.html, vignettes/mitoClone2/inst/doc/overview.html vignetteTitles: Computation of phylogenetic trees and clustering of mutations, Variant Calling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoClone2/inst/doc/clustering.R, vignettes/mitoClone2/inst/doc/overview.R dependencyCount: 118 Package: mixOmics Version: 6.20.0 Depends: R (>= 3.5.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl License: GPL (>= 2) MD5sum: 3e4ec9b12fbce2f58a0179ffeb5ae53e NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb, cre], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Al J Abadi URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_15 git_last_commit: dd98c6e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mixOmics_6.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mixOmics_6.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mixOmics_6.20.0.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: timeOmics, mixKernel, sgPLS importsMe: AlpsNMR, DepecheR, multiSight, POMA, MetabolomicsBasics, MSclassifR, plsmod, plsRcox, RVAideMemoire suggestsMe: autonomics, netOmics, SelectBoost, sharp dependencyCount: 67 Package: MLInterfaces Version: 1.76.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats, magrittr Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, testthat Enhances: parallel License: LGPL Archs: x64 MD5sum: 863499113eca66d99a688c50e4ae0853 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_15 git_last_commit: 935323d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MLInterfaces_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLInterfaces_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLInterfaces_1.76.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, dGAselID, nlcv dependencyCount: 111 Package: MLP Version: 1.44.0 Imports: AnnotationDbi, gplots, graphics, stats, utils Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: b6f05247392d9b8be2d99826d4f2b479 NeedsCompilation: no Title: Mean Log P Analysis Description: Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available. biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt [aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre], Katarzyna Gorczak [ctb] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_15 git_last_commit: 082509b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MLP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLP_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLP_1.44.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 49 Package: MLSeq Version: 2.14.0 Depends: caret, ggplot2 Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, e1071, kernlab License: GPL(>=2) MD5sum: 6f0a41a2713726f0d39136c048628db6 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: RELEASE_3_15 git_last_commit: 3add161 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MLSeq_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLSeq_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLSeq_2.14.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 153 Package: MMAPPR2 Version: 1.10.0 Depends: R (>= 3.6.0) Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation, BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools, magrittr, methods, grDevices, graphics, stats, utils, stringr, data.table Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle, MMAPPR2data License: GPL-3 OS_type: unix MD5sum: 56961d123d444bb772da5ec1f5bc509e NeedsCompilation: no Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens. Its predecessor is described in a paper published in Genome Research (Hill et al. 2013). MMAPPR2 accepts aligned BAM files as well as a reference genome as input, identifies loci of high sequence disparity between the control and mutant RNA sequences, predicts variant effects using Ensembl's Variant Effect Predictor, and outputs a ranked list of candidate mutations. biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill [cre] Maintainer: Jonathon Hill URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/, https://github.com/kjohnsen/MMAPPR2 SystemRequirements: Ensembl VEP, Samtools VignetteBuilder: knitr BugReports: https://github.com/kjohnsen/MMAPPR2/issues git_url: https://git.bioconductor.org/packages/MMAPPR2 git_branch: RELEASE_3_15 git_last_commit: bf96997 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MMAPPR2_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/MMAPPR2_1.10.0.tgz vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html vignetteTitles: An Introduction to MMAPPR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R dependencyCount: 105 Package: MMDiff2 Version: 1.24.0 Depends: R (>= 3.5.0), Rsamtools, Biobase Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: a968c30666fe9fc4c1dd47b5be3435f3 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: RELEASE_3_15 git_last_commit: d9a4cff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MMDiff2_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MMDiff2_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MMDiff2_1.24.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 97 Package: MMUPHin Version: 1.10.3 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, stringr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: e7062d9e440553e5d5933f899a0f8d71 NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: RELEASE_3_15 git_last_commit: de9cad1 git_last_commit_date: 2022-09-05 Date/Publication: 2022-09-06 source.ver: src/contrib/MMUPHin_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/MMUPHin_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.2/MMUPHin_1.10.3.tgz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 156 Package: mnem Version: 1.12.0 Depends: R (>= 4.1) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071, ggplot2, wesanderson LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM License: GPL-3 MD5sum: fb9a655cf6ad1ad9397c78850752c82a NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/mnem/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/mnem/issues git_url: https://git.bioconductor.org/packages/mnem git_branch: RELEASE_3_15 git_last_commit: 4e290e3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mnem_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mnem_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mnem_1.12.0.tgz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependsOnMe: nempi importsMe: bnem, dce, epiNEM dependencyCount: 82 Package: moanin Version: 1.4.0 Depends: R (>= 4.0), SummarizedExperiment, topGO, stats Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics, methods, grDevices, reshape2, NMI, zoo, ClusterR, splines, matrixStats Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, markdown, covr, BiocStyle License: BSD 3-clause License + file LICENSE MD5sum: 3717d2071e355c4797e1209b86f888e4 NeedsCompilation: no Title: An R Package for Time Course RNASeq Data Analysis Description: Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis. biocViews: TimeCourse, GeneExpression, RNASeq, Microarray, DifferentialExpression, Clustering Author: Elizabeth Purdom [aut] (), Nelle Varoquaux [aut, cre] () Maintainer: Nelle Varoquaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/moanin git_branch: RELEASE_3_15 git_last_commit: 9a7cc1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/moanin_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/moanin_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/moanin_1.4.0.tgz vignettes: vignettes/moanin/inst/doc/documentation.html vignetteTitles: The Moanin Package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/moanin/inst/doc/documentation.R dependencyCount: 94 Package: MobilityTransformR Version: 1.0.0 Depends: MSnbase, R (>= 4.2) Imports: xcms, MetaboCoreUtils, Spectra Suggests: testthat, msdata (>= 0.35.3), knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: e2578b9000852c456ce74feef80dc0e2 NeedsCompilation: no Title: Effective mobility scale transformation of CE-MS(/MS) data Description: MobilityTransformR collects a tool set for effective mobility scale transformation of CE-MS/MS data in order to increase reproducibility. It provides functionality to determine the migration times from mobility markers that have been added to the analysis and performs the transformation based on these markers. MobilityTransformR supports the conversion of numeric vectors, Spectra-objects, and MSnOnDiskExp. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics, Preprocessing Author: Liesa Salzer [cre, aut] () Maintainer: Liesa Salzer URL: https://github.com/LiesaSalzer/MobilityTransformR VignetteBuilder: knitr BugReports: https://github.com/LiesaSalzer/MobilityTransformR/issues git_url: https://git.bioconductor.org/packages/MobilityTransformR git_branch: RELEASE_3_15 git_last_commit: 850f91a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MobilityTransformR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MobilityTransformR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MobilityTransformR_1.0.0.tgz vignettes: vignettes/MobilityTransformR/inst/doc/MobilityTransformR.html vignetteTitles: Description and usage of MobilityTransformR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MobilityTransformR/inst/doc/MobilityTransformR.R dependencyCount: 95 Package: MODA Version: 1.22.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 17856d68be4e530d330bfe545f63b1d5 NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: RELEASE_3_15 git_last_commit: b9cee26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MODA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MODA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MODA_1.22.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 113 Package: ModCon Version: 1.4.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: 7082cff7ac8851cdcf95df8445788cb6 NeedsCompilation: no Title: Modifying splice site usage by changing the mRNP code, while maintaining the genetic code Description: Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] () Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: RELEASE_3_15 git_last_commit: 47a6ae6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ModCon_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ModCon_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ModCon_1.4.0.tgz vignettes: vignettes/ModCon/inst/doc/ModCon.html vignetteTitles: Designing SD context with ModCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ModCon/inst/doc/ModCon.R dependencyCount: 5 Package: Modstrings Version: 1.12.1 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 84fa10fdde56d16c30d916672f9feb6f NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: RELEASE_3_15 git_last_commit: 8b0f758 git_last_commit_date: 2022-08-13 Date/Publication: 2022-08-14 source.ver: src/contrib/Modstrings_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Modstrings_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Modstrings_1.12.1.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3 dependencyCount: 23 Package: MOFA2 Version: 1.6.0 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi Suggests: knitr, testthat, Seurat, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown License: GPL (>= 2) + file LICENSE MD5sum: d7f68a5b04da0d1eda0bd4e2ffa02716 NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut] (), Damien Arnol [aut] (), Danila Bredikhin [aut] (), Britta Velten [aut, cre] () Maintainer: Britta Velten URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 git_url: https://git.bioconductor.org/packages/MOFA2 git_branch: RELEASE_3_15 git_last_commit: 4f068dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MOFA2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOFA2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOFA2_1.6.0.tgz vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html, vignettes/MOFA2/inst/doc/getting_started_R.html, vignettes/MOFA2/inst/doc/MEFISTO_temporal.html vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a model in R, MEFISTO on simulated data (temporal) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R, vignettes/MOFA2/inst/doc/getting_started_R.R, vignettes/MOFA2/inst/doc/MEFISTO_temporal.R dependencyCount: 86 Package: MOGAMUN Version: 1.6.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 8aec47b4e151b0258d5b528d5c53507b NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] () Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: RELEASE_3_15 git_last_commit: 27e278a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MOGAMUN_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOGAMUN_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOGAMUN_1.6.0.tgz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 64 Package: mogsa Version: 1.30.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 MD5sum: 612d76cda941c07e8e633ccf483860be NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: RELEASE_3_15 git_last_commit: 24bb80c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mogsa_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mogsa_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mogsa_1.30.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 66 Package: MOMA Version: 1.8.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: a6f98286991acf5457b5c006a21db357 NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: RELEASE_3_15 git_last_commit: 6068fa6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MOMA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOMA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOMA_1.8.0.tgz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 95 Package: monaLisa Version: 1.2.0 Depends: R (>= 4.1) Imports: methods, stats, utils, grDevices, graphics, BiocGenerics, GenomicRanges, TFBSTools, Biostrings, IRanges, stabs, BSgenome, glmnet, S4Vectors, SummarizedExperiment, BiocParallel, grid, circlize, ComplexHeatmap (>= 2.11.1), XVector, GenomeInfoDb, tools, vioplot Suggests: JASPAR2020, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, testthat, BiocStyle, gridExtra License: GPL (>= 3) MD5sum: 92234eb23859b6d4424f090cb7a501d5 NeedsCompilation: no Title: Binned Motif Enrichment Analysis and Visualization Description: Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided. biocViews: MotifAnnotation, Visualization, FeatureExtraction, Epigenetics Author: Dania Machlab [aut] (), Lukas Burger [aut], Charlotte Soneson [aut] (), Michael Stadler [aut, cre] () Maintainer: Michael Stadler URL: https://github.com/fmicompbio/monaLisa, https://bioconductor.org/packages/monaLisa/, https://fmicompbio.github.io/monaLisa/ VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/monaLisa/issues git_url: https://git.bioconductor.org/packages/monaLisa git_branch: RELEASE_3_15 git_last_commit: 2b27273 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/monaLisa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/monaLisa_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/monaLisa_1.2.0.tgz vignettes: vignettes/monaLisa/inst/doc/monaLisa.html, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html vignetteTitles: monaLisa - MOtif aNAlysis with Lisa, selecting_motifs_with_randLassoStabSel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R dependencyCount: 140 Package: monocle Version: 2.24.1 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>= 0.1.9), limma, tibble, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 MD5sum: 07a95b36befabeaba5f5734d5b247343 NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: RELEASE_3_15 git_last_commit: 77f953c git_last_commit_date: 2022-06-08 Date/Publication: 2022-06-09 source.ver: src/contrib/monocle_2.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/monocle_2.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/monocle_2.24.1.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero, ctgGEM, phemd importsMe: uSORT suggestsMe: M3Drop, scran, sincell, grandR, Seurat dependencyCount: 80 Package: MoonlightR Version: 1.22.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png License: GPL (>= 3) MD5sum: d941a05dbdd0de77ad8d965c6b0bc8bd NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ELELAB/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_15 git_last_commit: 07263ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MoonlightR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MoonlightR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MoonlightR_1.22.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 181 Package: mosaics Version: 2.34.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: x64 MD5sum: 3a79e57c4fa10f44de145b3d85e24f13 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: RELEASE_3_15 git_last_commit: 572f11a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mosaics_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mosaics_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mosaics_2.34.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 42 Package: mosbi Version: 1.2.0 Depends: R (>= 4.1) Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust, isa2, QUBIC, akmbiclust, RColorBrewer LinkingTo: Rcpp, BH, RcppParallel Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 373f1b9a03e2358e40495637aa20384c NeedsCompilation: yes Title: Molecular Signature identification using Biclustering Description: This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms. biocViews: Software, StatisticalMethod, Clustering, Network Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut], Nikolai Koehler [aut] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: RELEASE_3_15 git_last_commit: 7649cab git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mosbi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mosbi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mosbi_1.2.0.tgz vignettes: vignettes/mosbi/inst/doc/example-workflow.html, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.html vignetteTitles: example-workflow, similarity-metrics-evaluation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosbi/inst/doc/example-workflow.R, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.R dependencyCount: 62 Package: MOSim Version: 1.10.0 Depends: R (>= 3.6) Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi, lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble, tidyr, ggplot2, Biobase, IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 37fabb53e25f96459c01237d9c709610 NeedsCompilation: no Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carlos Martínez [cre, aut], Sonia Tarazona [aut] Maintainer: Carlos Martínez URL: https://github.com/Neurergus/MOSim VignetteBuilder: knitr BugReports: https://github.com/Neurergus/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_15 git_last_commit: 6d4b6fe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MOSim_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOSim_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOSim_1.10.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf vignetteTitles: MOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R dependencyCount: 55 Package: Motif2Site Version: 1.0.0 Depends: R (>= 4.1) Imports: S4Vectors, stats, utils, methods, grDevices, graphics, BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges, GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805 License: GPL-2 Archs: x64 MD5sum: 289ae8a1053d2cbab94a569150f43041 NeedsCompilation: no Title: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions Description: Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves. biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling, Epigenetics, SequenceMatching Author: Peyman Zarrineh [cre, aut] () Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/ManchesterBioinference/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: RELEASE_3_15 git_last_commit: 5ea499c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Motif2Site_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Motif2Site_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Motif2Site_1.0.0.tgz vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html vignetteTitles: Motif2Site hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R dependencyCount: 57 Package: motifbreakR Version: 2.10.2 Depends: R (>= 4.1.0), grid, MotifDb Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: 4fe3e984afe8dd8a3d4ee4980ff17553 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 32 species, a total of 109 versions). biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription Author: Simon Gert Coetzee [aut, cre], Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: RELEASE_3_15 git_last_commit: 8ae42bb git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-13 source.ver: src/contrib/motifbreakR_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifbreakR_2.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/motifbreakR_2.10.2.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 174 Package: motifcounter Version: 1.20.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 MD5sum: 12fa460cb595d003cc95da5faf0b3848 NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: RELEASE_3_15 git_last_commit: 56e0f21 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/motifcounter_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/motifcounter_1.20.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 18 Package: MotifDb Version: 1.38.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR, markdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: f612e3331a6bd220c25f415ef24d4218 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_15 git_last_commit: d45f2bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MotifDb_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MotifDb_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MotifDb_1.38.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, trena, generegulation importsMe: igvR, rTRMui suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, memes, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 47 Package: motifmatchr Version: 1.18.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE MD5sum: 179eeee2ed32d3618faa84a9dfad33e9 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: RELEASE_3_15 git_last_commit: f733bc3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/motifmatchr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifmatchr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/motifmatchr_1.18.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: enhancerHomologSearch, enrichTF, esATAC, pageRank, spatzie suggestsMe: chromVAR, MethReg, CAGEWorkflow, Signac dependencyCount: 124 Package: motifStack Version: 1.40.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML, TFBSTools Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020 License: GPL (>= 2) MD5sum: b294cb36d9c2129b9fe4bd7d3064b8fb NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_15 git_last_commit: 175f1b5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/motifStack_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifStack_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/motifStack_1.40.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, LowMACA, motifbreakR, ribosomeProfilingQC, TCGAWorkflow suggestsMe: ChIPpeakAnno, TFutils, tripr, universalmotif dependencyCount: 129 Package: MouseFM Version: 1.6.0 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 05ac86dd893db57e72256797232f8776 NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (), Inken Wohlers [aut] (), Hauke Busch [aut] () Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: RELEASE_3_15 git_last_commit: 50e5d9c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MouseFM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MouseFM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MouseFM_1.6.0.tgz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 97 Package: MPFE Version: 1.32.0 License: GPL (>= 3) MD5sum: fe9566380298c101420044e47142a906 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: RELEASE_3_15 git_last_commit: 7e50c58 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MPFE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MPFE_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MPFE_1.32.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.18.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 49fffac9b09624438114b012ef286509 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: RELEASE_3_15 git_last_commit: 95285f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mpra_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mpra_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mpra_1.18.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 39 Package: MPRAnalyze Version: 1.14.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: dc4fcd12e5558ccddeb66fe2090be5dc NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression, ExperimentalDesign, Classification Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer [ctb], Fabian J Theis [ctb], Nir Yosef [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: RELEASE_3_15 git_last_commit: bc09f92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MPRAnalyze_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MPRAnalyze_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MPRAnalyze_1.14.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 46 Package: MQmetrics Version: 1.4.0 Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra, utils, stringr, ggpubr, stats, cowplot, RColorBrewer, tidyr, scales, grid, rlang, ggforce, grDevices, gtable, plyr, knitr, rmarkdown, ggrepel, gghalves, tools Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 4ce3eaf699a762a98726620d87f9486e NeedsCompilation: no Title: Quality Control of Protemics Data Description: The package MQmetrics (MaxQuant metrics) provides a workflow to analyze the quality and reproducibility of your proteomics mass spectrometry analysis from MaxQuant.Input data are extracted from several MaxQuant output tables and produces a pdf report. It includes several visualization tools to check numerous parameters regarding the quality of the runs. It also includes two functions to visualize the iRT peptides from Biognosys in case they were spiked in the samples. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Alvaro Sanchez-Villalba [aut, cre], Thomas Stehrer [aut], Marek Vrbacky [aut] Maintainer: Alvaro Sanchez-Villalba VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MQmetrics git_branch: RELEASE_3_15 git_last_commit: 2c8f6d8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MQmetrics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MQmetrics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MQmetrics_1.4.0.tgz vignettes: vignettes/MQmetrics/inst/doc/MQmetrics.html vignetteTitles: MQmetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MQmetrics/inst/doc/MQmetrics.R dependencyCount: 124 Package: msa Version: 1.28.0 Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn License: GPL (>= 2) MD5sum: 7b55febbdc6607fc08a76aed533ec0b4 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_15 git_last_commit: b9511f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msa_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msa_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msa_1.28.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq, surfaltr suggestsMe: idpr, bio3d, datelife dependencyCount: 19 Package: MSA2dist Version: 1.0.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel, dplyr, foreach, methods, parallel, rlang, seqinr, stringr, tibble, tidyr, stats, stringi LinkingTo: Rcpp, RcppThread Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle License: MIT + file LICENSE MD5sum: 2880dd84e35031a23944f9eca2b1e58e NeedsCompilation: yes Title: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis Description: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calcualtes pairwise IUPAC distances. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] () Maintainer: Kristian K Ullrich URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist, https://mpievolbio-it.pages.gwdg.de/MSA2dist/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues git_url: https://git.bioconductor.org/packages/MSA2dist git_branch: RELEASE_3_15 git_last_commit: 007f29b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSA2dist_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSA2dist_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSA2dist_1.0.0.tgz vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html vignetteTitles: MSA2dist Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R dependencyCount: 58 Package: MsBackendMassbank Version: 1.4.0 Depends: R (>= 4.0), Spectra (>= 1.5.17) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics, MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: b94e2da9751372a59ad1ee97cdc37209 NeedsCompilation: no Title: Mass Spectrometry Data Backend for MassBank record Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting [aut] (), Johannes Rainer [aut] (), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMassbank VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMassbank/issues git_url: https://git.bioconductor.org/packages/MsBackendMassbank git_branch: RELEASE_3_15 git_last_commit: 5b15c06 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsBackendMassbank_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMassbank_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMassbank_1.4.0.tgz vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html vignetteTitles: Description and usage of MsBackendMassbank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R dependencyCount: 28 Package: MsBackendMgf Version: 1.4.0 Depends: R (>= 4.0), Spectra (>= 1.5.14) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: e670010c2821dfc46b71e39c7a0770dc NeedsCompilation: no Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf) Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] () Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMgf VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues git_url: https://git.bioconductor.org/packages/MsBackendMgf git_branch: RELEASE_3_15 git_last_commit: 47a1909 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsBackendMgf_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMgf_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMgf_1.4.0.tgz vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html vignetteTitles: Description and usage of MsBackendMgf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R suggestsMe: MsBackendRawFileReader, xcms dependencyCount: 27 Package: MsBackendMsp Version: 1.0.0 Depends: R (>= 4.1.0), Spectra (>= 1.5.14) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: d8a6c351f902ea1ab551fd454e423f9b NeedsCompilation: no Title: Mass Spectrometry Data Backend for NIST msp Files Description: Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: Neumann Steffen [aut] (), Johannes Rainer [aut, cre] (), Michael Witting [ctb] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMsp VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues git_url: https://git.bioconductor.org/packages/MsBackendMsp git_branch: RELEASE_3_15 git_last_commit: 06bd689 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsBackendMsp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMsp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMsp_1.0.0.tgz vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html vignetteTitles: MsBackendMsp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R dependencyCount: 27 Package: MsBackendRawFileReader Version: 1.2.0 Depends: R (>= 4.1), methods, Spectra (>= 1.5.8) Imports: MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.3.6), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: e2f0b22e3fbbb564e11a9e5f4bd37c82 NeedsCompilation: yes Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files Description: implements a MsBackend for the Spectra package using Thermo Fisher Scientific's NewRawFileReader .Net libraries. The package is generalizing the functionality introduced by the rawrr package (Kockmann T. et al. (2020) ) Methods defined in this package are supposed to extend the Spectra Bioconductor package. biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (), Tobias Kockmann [aut] () Maintainer: Christian Panse URL: https://github.com/fgcz/MsBackendRawFileReader SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader git_branch: RELEASE_3_15 git_last_commit: 6ea4402 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsBackendRawFileReader_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendRawFileReader_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendRawFileReader_1.2.0.tgz vignettes: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html vignetteTitles: On Using and Extending the `MsBackendRawFileReader` Backend. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R dependencyCount: 28 Package: MsCoreUtils Version: 1.8.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, Matrix, preprocessCore License: Artistic-2.0 MD5sum: 817486ce7a307ad13ce81846bdfe1e29 NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (), Michael Witting [ctb] (), Samuel Wieczorek [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: RELEASE_3_15 git_last_commit: 8b7e2c3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsCoreUtils_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsCoreUtils_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsCoreUtils_1.8.0.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, MetaboCoreUtils, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsFeatures, MSnbase, PSMatch, QFeatures, qmtools, scp, Spectra, xcms suggestsMe: MetNet, msqrob2 dependencyCount: 12 Package: MsFeatures Version: 1.4.0 Depends: R (>= 4.1) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: 97cf27743ee9038d2ecfed2877c14038 NeedsCompilation: no Title: Functionality for Mass Spectrometry Features Description: The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. biocViews: Infrastructure, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues git_url: https://git.bioconductor.org/packages/MsFeatures git_branch: RELEASE_3_15 git_last_commit: 892badc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MsFeatures_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsFeatures_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsFeatures_1.4.0.tgz vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html vignetteTitles: Grouping Mass Spectrometry Features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R importsMe: xcms suggestsMe: qmtools dependencyCount: 31 Package: msgbsR Version: 1.20.0 Depends: R (>= 3.5.0), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 Archs: x64 MD5sum: 4989730226809c183970e39679652db3 NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_15 git_last_commit: acb23f0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msgbsR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msgbsR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msgbsR_1.20.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 169 Package: msImpute Version: 1.6.0 Depends: R (>= 3.5.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr, dplyr Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: 013f1f503da96e82f5a81827de88414f NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP). biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] () Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: RELEASE_3_15 git_last_commit: bd9c63d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msImpute_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msImpute_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msImpute_1.6.0.tgz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 89 Package: msmsEDA Version: 1.34.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: fdeacda8400b2a64fdbde49a6e991141 NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: RELEASE_3_15 git_last_commit: ab9476f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msmsEDA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msmsEDA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msmsEDA_1.34.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman, RforProteomics dependencyCount: 80 Package: msmsTests Version: 1.34.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 78e49f8f9a0b423ec68d643e04b31791 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: RELEASE_3_15 git_last_commit: d657607 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msmsTests_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msmsTests_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msmsTests_1.34.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R importsMe: MSnID suggestsMe: RforProteomics dependencyCount: 88 Package: MSnbase Version: 2.22.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.27.2) Imports: MsCoreUtils, BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, XML, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 MD5sum: 657ca79831deedd9eb2de05b96a8c14e NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_15 git_last_commit: 4f6e576 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSnbase_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSnbase_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSnbase_2.22.0.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: bandle, MetCirc, MobilityTransformR, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, synapter, xcms, DAPARdata, pRolocdata, RforProteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, peakPantheR, PrInCE, Prostar, ptairMS, topdownr, qPLEXdata suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, pmd, RAMClustR dependencyCount: 74 Package: MSnID Version: 1.30.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 MD5sum: 06b0c6fdb383d26c175549fa641ee03e NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: RELEASE_3_15 git_last_commit: a4e5857 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSnID_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSnID_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSnID_1.30.0.tgz vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf, vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: Handling Modifications with MSnID, MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/handling_mods.R, vignettes/MSnID/inst/doc/msnid_vignette.R suggestsMe: RforProteomics dependencyCount: 160 Package: MSPrep Version: 1.6.0 Depends: R (>= 4.1.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), crmn, preprocessCore, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, missForest, sva, VIM, Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 Archs: x64 MD5sum: 83dd574b7e58d1f72f0f8f2537873ef3 NeedsCompilation: no Title: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data Description: Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. biocViews: Metabolomics, MassSpectrometry, Preprocessing Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes [aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut], Katerina Kechris [aut, cph, ths] Maintainer: Max McGrath URL: https://github.com/KechrisLab/MSPrep VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MSPrep/issues git_url: https://git.bioconductor.org/packages/MSPrep git_branch: RELEASE_3_15 git_last_commit: 4f27af9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSPrep_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSPrep_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSPrep_1.6.0.tgz vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html vignetteTitles: Using MSPrep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R dependencyCount: 161 Package: msPurity Version: 1.22.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: efb5a036979bb3caac71030dabb998f8 NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_15 git_last_commit: 948eafd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msPurity_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msPurity_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msPurity_1.22.0.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 70 Package: msqrob2 Version: 1.4.0 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, codetools Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata, MSnbase, matrixStats, MsCoreUtils License: Artistic-2.0 MD5sum: b201528d28e52aab108022f507d671d2 NeedsCompilation: no Title: Robust statistical inference for quantitative LC-MS proteomics Description: msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, MultipleComparison, Regression, ExperimentalDesign, Software, ImmunoOncology, Normalization, TimeCourse, Preprocessing Author: Lieven Clement [aut, cre] (), Laurent Gatto [aut] (), Oliver M. Crook [aut] (), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement URL: https://github.com/statOmics/msqrob2 VignetteBuilder: knitr BugReports: https://github.com/statOmics/msqrob2/issues git_url: https://git.bioconductor.org/packages/msqrob2 git_branch: RELEASE_3_15 git_last_commit: 042dbaf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/msqrob2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msqrob2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msqrob2_1.4.0.tgz vignettes: vignettes/msqrob2/inst/doc/cptac.html vignetteTitles: A. label-free workflow with two group design hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msqrob2/inst/doc/cptac.R dependencyCount: 117 Package: MSstats Version: 4.4.1 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, marray, stats, grDevices, graphics, methods LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown License: Artistic-2.0 MD5sum: 67bff2f30d3f2ed3ac7d3c9fd441d669 NeedsCompilation: yes Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_15 git_last_commit: e5dae55 git_last_commit_date: 2022-05-11 Date/Publication: 2022-05-15 source.ver: src/contrib/MSstats_4.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstats_4.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstats_4.4.1.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R importsMe: artMS, MSstatsLiP, MSstatsPTM, MSstatsSampleSize, MSstatsTMT dependencyCount: 78 Package: MSstatsConvert Version: 1.6.0 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 MD5sum: 0855437307c6fe97ffc3a1833b810ec1 NeedsCompilation: no Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut, cre], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: RELEASE_3_15 git_last_commit: 346ff80 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsConvert_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsConvert_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsConvert_1.6.0.tgz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R importsMe: MSstats, MSstatsLiP, MSstatsPTM, MSstatsTMT dependencyCount: 9 Package: MSstatsLiP Version: 1.2.1 Depends: R (>= 4.1) Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats, MSstatsConvert, data.table, Biostrings, MSstatsPTM, Rcpp, checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse, scales, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight License: Artistic-2.0 Archs: x64 MD5sum: d5c6a16924496d39554c30488dc4406f NeedsCompilation: yes Title: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments Description: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: RELEASE_3_15 git_last_commit: 87a9dd6 git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-28 source.ver: src/contrib/MSstatsLiP_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsLiP_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsLiP_1.2.1.tgz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R dependencyCount: 196 Package: MSstatsLOBD Version: 1.4.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 MD5sum: f59c47f1cb887715acff6a52a85714d5 NeedsCompilation: no Title: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD) Description: The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) . biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: RELEASE_3_15 git_last_commit: 48b84a5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsLOBD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsLOBD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsLOBD_1.4.0.tgz vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html vignetteTitles: LOB/LOD Estimation Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R dependencyCount: 38 Package: MSstatsPTM Version: 1.6.0 Depends: R (>= 4.0) Imports: dplyr, gridExtra, stringr, stats, ggplot2, grDevices, MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr License: Artistic-2.0 Archs: x64 MD5sum: 3ac03b9186b136949878609a01e70ca7 NeedsCompilation: yes Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: RELEASE_3_15 git_last_commit: e7a3639 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsPTM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsPTM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsPTM_1.6.0.tgz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP dependencyCount: 97 Package: MSstatsQC Version: 2.14.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 MD5sum: f5b5e3d72867480c8c68c0a99a7974b7 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: RELEASE_3_15 git_last_commit: a7cb4a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsQC_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsQC_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsQC_2.14.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 127 Package: MSstatsQCgui Version: 1.16.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 Archs: x64 MD5sum: facff52cb84bc42ab2f6534084c95096 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: RELEASE_3_15 git_last_commit: b2ae741 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsQCgui_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsQCgui_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsQCgui_1.16.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 129 Package: MSstatsSampleSize Version: 1.10.0 Depends: R (>= 3.6) Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats, utils, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 921b671d9e06100b4ae89be1ff06a92a NeedsCompilation: no Title: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment Description: The packages estimates the variance in the input protein abundance data and simulates data with predefined number of biological replicates based on the variance estimation. It reports the mean predictive accuracy of the classifier and mean protein importance over multiple iterations of the simulation. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression, Classification, PrincipalComponent, ExperimentalDesign, Visualization Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsSampleSize git_branch: RELEASE_3_15 git_last_commit: 075aab8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MSstatsSampleSize_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsSampleSize_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsSampleSize_1.10.0.tgz vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html vignetteTitles: MSstatsSampleSize User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R dependencyCount: 124 Package: MSstatsTMT Version: 2.4.1 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 03d1d23a12e1e36fa35096d4943efa39 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Ting Huang [aut, cre], Meena Choi [aut], Mateusz Staniak [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: RELEASE_3_15 git_last_commit: 8d23d7b git_last_commit_date: 2022-09-13 Date/Publication: 2022-09-13 source.ver: src/contrib/MSstatsTMT_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsTMT_2.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsTMT_2.4.1.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsPTM dependencyCount: 81 Package: MuData Version: 1.0.0 Depends: Matrix, S4Vectors, rhdf5 Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: e36279a6f823450164cb00e1df4246c8 NeedsCompilation: no Title: Serialization for MultiAssayExperiment Objects Description: Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. biocViews: DataImport Author: Danila Bredikhin [aut, cre] (), Ilia Kats [aut] () Maintainer: Danila Bredikhin URL: https://github.com/ilia-kats/MuData VignetteBuilder: knitr BugReports: https://github.com/ilia-kats/MuData/issues git_url: https://git.bioconductor.org/packages/MuData git_branch: RELEASE_3_15 git_last_commit: d509fb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MuData_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MuData_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MuData_1.0.0.tgz vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html, vignettes/MuData/inst/doc/Getting-Started.html vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with MuData, Getting started with MuDataMae hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R, vignettes/MuData/inst/doc/Getting-Started.R dependencyCount: 50 Package: Mulcom Version: 1.46.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: 7ca618d3ec247e08d48841182fed4b8b NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: RELEASE_3_15 git_last_commit: 87e44ac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Mulcom_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mulcom_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mulcom_1.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 44 Package: MultiAssayExperiment Version: 1.22.0 Depends: R (>= 4.2.0), SummarizedExperiment (>= 1.3.81) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, S4Vectors (>= 0.23.19), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, HDF5Array (>= 1.19.17), knitr, maftools (>= 2.7.10), rmarkdown, R.rsp, RaggedExperiment, UpSetR, survival, survminer, testthat License: Artistic-2.0 MD5sum: dddaddd78a8431bbe00774d1202cdb0a NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: MultiAssayExperiment harmonizes data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre] (), Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr, R.rsp Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: RELEASE_3_15 git_last_commit: e1aeb73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MultiAssayExperiment_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiAssayExperiment_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiAssayExperiment_1.22.0.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: CAGEr, cBioPortalData, ClassifyR, evaluomeR, glmSparseNet, hipathia, InTAD, mia, midasHLA, missRows, QFeatures, terraTCGAdata, TimiRGeN, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, GOpro, hermes, LinkHD, metabolomicsWorkbenchR, MOMA, MuData, MultiBaC, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, ropls, scp, TCGAutils, xcore, curatedTBData, HMP2Data suggestsMe: BiocOncoTK, CNVRanger, deco, maftools, MOFA2, MultiDataSet, RaggedExperiment, brgedata, MOFAdata dependencyCount: 45 Package: MultiBaC Version: 1.6.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 Archs: x64 MD5sum: 28c664e0d4c10f35bbcdbe0f4f5378b1 NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: RELEASE_3_15 git_last_commit: b1bcb11 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MultiBaC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiBaC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiBaC_1.6.0.tgz vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html vignetteTitles: MultiBaC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R dependencyCount: 70 Package: multiClust Version: 1.26.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 2f1405dddc028ead5cec257d309a6fbe NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: RELEASE_3_15 git_last_commit: 6bcff9f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiClust_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiClust_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiClust_1.26.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 45 Package: multicrispr Version: 1.6.0 Depends: R (>= 4.0) Imports: assertive, BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 MD5sum: d0676739e19cb5f0a3963e9288e12e6c NeedsCompilation: no Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Rene Wiegandt [ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/loosolab/multicrispr VignetteBuilder: knitr BugReports: https://github.com/loosolab/multicrispr/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: RELEASE_3_15 git_last_commit: 0f3e6be git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multicrispr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multicrispr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multicrispr_1.6.0.tgz vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html, vignettes/multicrispr/inst/doc/genome_arithmetics.html, vignettes/multicrispr/inst/doc/prime_editing.html vignetteTitles: grna_design, genome_arithmetics, prime_editing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R, vignettes/multicrispr/inst/doc/genome_arithmetics.R, vignettes/multicrispr/inst/doc/prime_editing.R dependencyCount: 193 Package: MultiDataSet Version: 1.24.0 Depends: R (>= 4.1), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: 72232fab809177bb0b36b2f725b5fc69 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: RELEASE_3_15 git_last_commit: 618a839 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MultiDataSet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiDataSet_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiDataSet_1.24.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, ropls dependencyCount: 58 Package: multiGSEA Version: 1.6.0 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, metaboliteIDmapping, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: 0910d0d94dfe040301ba931fb6e995a8 NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (), Jörg Hackermüller [aut] () Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: RELEASE_3_15 git_last_commit: 40497d5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiGSEA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiGSEA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiGSEA_1.6.0.tgz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 112 Package: multiHiCcompare Version: 1.14.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 9685d6b4a62a8895fa5015e3784e6679 NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: RELEASE_3_15 git_last_commit: 1c6969d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiHiCcompare_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiHiCcompare_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiHiCcompare_1.14.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: juiceboxVisualization, multiHiCcompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R suggestsMe: HiCcompare dependencyCount: 102 Package: MultiMed Version: 2.18.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: ee3111d58af65c209028a52354eff128 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: RELEASE_3_15 git_last_commit: 29d95a4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MultiMed_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiMed_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiMed_2.18.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.18.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: ca9381fac46233c9cdd9450ef8de667e NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: RELEASE_3_15 git_last_commit: 59a9d0c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiMiR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiMiR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiMiR_1.18.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R dependencyCount: 57 Package: multiOmicsViz Version: 1.20.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: d0fece20a8ef7ae19a57fc2e5a28cab9 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/multiOmicsViz git_branch: RELEASE_3_15 git_last_commit: 964846d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiOmicsViz_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiOmicsViz_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiOmicsViz_1.20.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R dependencyCount: 30 Package: multiscan Version: 1.56.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 1984d71ebd684de55f5bdc9a61fe1b4b NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: RELEASE_3_15 git_last_commit: ebb7736 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multiscan_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiscan_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiscan_1.56.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 6 Package: multiSight Version: 1.4.0 Depends: R (>= 4.1) Imports: golem, config, R6, shiny, shinydashboard, DT, dplyr, stringr, anyLib, caret, biosigner, mixOmics, stats, DESeq2, clusterProfiler, rWikiPathways, ReactomePA, enrichplot, ppcor, metap, infotheo, igraph, networkD3, easyPubMed, utils, htmltools, rmarkdown, ggnewscale Suggests: org.Mm.eg.db, rlang, markdown, attempt, processx, testthat, knitr, BiocStyle License: CeCILL + file LICENSE MD5sum: 3f8fd599eb9a372ee5f032a9b0a90e03 NeedsCompilation: no Title: Multi-omics Classification, Functional Enrichment and Network Inference analysis Description: multiSight is an R package providing functions to analyze your omic datasets in a multi-omics manner based on Stouffer's p-value pooling and multi-block statistical methods. For each omic dataset you furnish, multiSight provides classification models with feature selection you can use as biosignature: (i) To forecast phenotypes (e.g. to diagnostic tasks, histological subtyping), (ii) To design Pathways and gene ontology enrichments (Over Representation Analysis), (iii) To build Network inference linked to PubMed querying to make assumptions easier and data-driven. Main analysis are embedded in an user-friendly graphical interface. biocViews: Software, RNASeq, miRNA, Network, NetworkInference, DifferentialExpression, Classification, Pathways, GeneSetEnrichment Author: Florian Jeanneret [cre, aut] (), Stephane Gazut [aut] Maintainer: Florian Jeanneret VignetteBuilder: knitr BugReports: https://github.com/Fjeanneret/multiSight/issues git_url: https://git.bioconductor.org/packages/multiSight git_branch: RELEASE_3_15 git_last_commit: 9771d24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/multiSight_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiSight_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiSight_1.4.0.tgz vignettes: vignettes/multiSight/inst/doc/multiSight.html vignetteTitles: multiSight quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiSight/inst/doc/multiSight.R dependencyCount: 283 Package: multtest Version: 2.52.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: x64 MD5sum: 04aa8a68d8db612edd3f1ed397403ecc NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: RELEASE_3_15 git_last_commit: 00cfc9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/multtest_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multtest_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multtest_2.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, IsoGeneGUI, mAPKL, metabomxtr, microbiomeMarker, nethet, OCplus, phyloseq, RTopper, SingleCellSignalR, singleCellTK, synapter, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, ecolitk, factDesign, GOstats, GSEAlm, maigesPack, ropls, topGO, xcms, cherry, POSTm dependencyCount: 14 Package: mumosa Version: 1.4.0 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 MD5sum: d76fe170b7824f50d48c0f6847eb63ca NeedsCompilation: no Title: Multi-Modal Single-Cell Analysis Methods Description: Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities. biocViews: ImmunoOncology, SingleCell, RNASeq Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: http://bioconductor.org/packages/mumosa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/mumosa git_branch: RELEASE_3_15 git_last_commit: 90ed1e6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mumosa_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mumosa_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mumosa_1.4.0.tgz vignettes: vignettes/mumosa/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mumosa/inst/doc/overview.R dependsOnMe: OSCA.advanced dependencyCount: 66 Package: MungeSumstats Version: 1.4.5 Depends: R(>= 4.1) Imports: magrittr, data.table, utils, R.utils, dplyr, stats, GenomicRanges, IRanges, GenomeInfoDb, BSgenome, Biostrings, VariantAnnotation, googleAuthR, httr, jsonlite, methods, parallel, rtracklayer, RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, seqminer, Rsamtools, MatrixGenerics, badger License: Artistic-2.0 Archs: x64 MD5sum: 3122cb61fe1e32c6b211dfe2629f2027 NeedsCompilation: no Title: Standardise summary statistics from GWAS Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [aut, cre] (), Brian Schilder [aut, ctb] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: RELEASE_3_15 git_last_commit: 0da13c2 git_last_commit_date: 2022-06-08 Date/Publication: 2022-06-09 source.ver: src/contrib/MungeSumstats_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/MungeSumstats_1.4.5.zip mac.binary.ver: bin/macosx/contrib/4.2/MungeSumstats_1.4.5.tgz vignettes: vignettes/MungeSumstats/inst/doc/docker.html, vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: docker, MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/docker.R, vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 107 Package: muscat Version: 1.10.1 Depends: R (>= 4.2) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, S4Vectors, scales, scater, scuttle, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition, viridis Suggests: BiocStyle, countsimQC, cowplot, ExperimentHub, iCOBRA, knitr, phylogram, RColorBrewer, reshape2, rmarkdown, statmod, testthat, UpSetR License: GPL-3 MD5sum: 28964830aae46a21c915ee9a20a86e7c NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues git_url: https://git.bioconductor.org/packages/muscat git_branch: RELEASE_3_15 git_last_commit: 4c1ba70 git_last_commit_date: 2022-06-17 Date/Publication: 2022-06-21 source.ver: src/contrib/muscat_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/muscat_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/muscat_1.10.1.tgz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "2. Data simulation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: muscData dependencyCount: 187 Package: muscle Version: 3.38.0 Depends: Biostrings License: Unlimited MD5sum: 5af349a37379e0fbf993354d014302ac NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: RELEASE_3_15 git_last_commit: c4bc9fd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/muscle_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/muscle_3.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/muscle_3.38.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R importsMe: geneHapR, ptm suggestsMe: seqmagick dependencyCount: 18 Package: musicatk Version: 1.6.0 Depends: R (>= 4.0.0), NMF Imports: SummarizedExperiment, VariantAnnotation, cowplot, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, deconstructSigs, decompTumor2Sig, topicmodels, ggrepel, withr, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, shinydashboard, sortable, maftools, shiny, shinyjs, shinyalert, shinybusy, shinyBS, TCGAbiolinks, shinyjqui, stringi Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr License: LGPL-3 MD5sum: 4937cd2859928e22c26cfc3033f2902a NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [cre] (0000-0002-3968-9250), Joshua D. Campbell [aut] () Maintainer: Aaron Chevalier VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: RELEASE_3_15 git_last_commit: a04eaa9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/musicatk_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/musicatk_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/musicatk_1.6.0.tgz vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 273 Package: MutationalPatterns Version: 3.6.0 Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: a11e4d15df00f24e0fcf7d1b2eb26806 NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (), Francis Blokzijl [aut] (), Roel Janssen [aut] (), Jurrian de Kanter [ctb] (), Rurika Oka [ctb] (), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (), Edwin Cuppen [aut] () Maintainer: Mark van Roosmalen URL: https://doi.org/doi:10.1186/s12864-022-08357-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_15 git_last_commit: 4908f42 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MutationalPatterns_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MutationalPatterns_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MutationalPatterns_3.6.0.tgz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R dependencyCount: 133 Package: MVCClass Version: 1.70.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: d1cb7a63cd00e8025d4e023e70fbff9d NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: RELEASE_3_15 git_last_commit: 9b1d2d8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MVCClass_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MVCClass_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MVCClass_1.70.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.20.0 Depends: R (>= 3.5.0) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 7a53825f711c7576fca420dbbc3704f4 NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: RELEASE_3_15 git_last_commit: d7bca7b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MWASTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MWASTools_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MWASTools_1.20.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 136 Package: mygene Version: 1.32.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: 4eb3dffe18c4f849ce8e098ccc41f3b0 NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_15 git_last_commit: a9dfdfc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mygene_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mygene_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mygene_1.32.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal dependencyCount: 142 Package: myvariant Version: 1.26.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: b89960b7c96071388f60f85cc11ca6fe NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: RELEASE_3_15 git_last_commit: 3930153 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/myvariant_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/myvariant_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/myvariant_1.26.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 140 Package: mzID Version: 1.34.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 12a6ed47ef550fabbfbbd609b0f62e9b NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (), Thomas Pedersen [aut] (), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: RELEASE_3_15 git_last_commit: bef64db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mzID_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mzID_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mzID_1.34.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSnbase, MSnID, TargetDecoy suggestsMe: mzR, PSMatch, RforProteomics dependencyCount: 11 Package: mzR Version: 2.30.0 Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: ebc467f55063f8de248e7c9062affc25 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: RELEASE_3_15 git_last_commit: 563ae75 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/mzR_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mzR_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mzR_2.30.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSnbase importsMe: adductomicsR, CluMSID, DIAlignR, MSnID, msPurity, peakPantheR, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, MsBackendRawFileReader, PSMatch, qcmetrics, Spectra, msdata, RforProteomics, chromConverter, erah dependencyCount: 10 Package: NADfinder Version: 1.20.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager, rmarkdown License: GPL (>= 2) MD5sum: ca84cf3a0cf473580266be4e3c539b18 NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: RELEASE_3_15 git_last_commit: bc016dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NADfinder_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NADfinder_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NADfinder_1.20.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 242 Package: NanoMethViz Version: 2.2.0 Depends: R (>= 4.0.0), methods, ggplot2 Imports: cpp11 (>= 0.2.5), readr, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, data.table, e1071, fs, GenomicRanges, glue, limma (>= 3.44.0), patchwork, purrr, rlang, RSQLite, Rsamtools, scales, scico, stats, stringr, tibble, tidyr, utils, withr, zlibbioc LinkingTo: Rcpp Suggests: DSS, Mus.musculus, Homo.sapiens, knitr, rmarkdown, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) Archs: x64 MD5sum: c5791dc1fccd80fdc75129063b622d04 NeedsCompilation: yes Title: Visualise methlation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, Visualization, DifferentialMethylation Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: RELEASE_3_15 git_last_commit: ccc98e8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NanoMethViz_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoMethViz_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoMethViz_2.2.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.html, vignettes/NanoMethViz/inst/doc/ExonAnnotations.html, vignettes/NanoMethViz/inst/doc/ImportingExportingData.html, vignettes/NanoMethViz/inst/doc/Introduction.html vignetteTitles: Dimensionality Reduction, Exon Annotations, Importing/Exporting Data, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.R, vignettes/NanoMethViz/inst/doc/ExonAnnotations.R, vignettes/NanoMethViz/inst/doc/ImportingExportingData.R, vignettes/NanoMethViz/inst/doc/Introduction.R dependencyCount: 138 Package: NanoStringDiff Version: 1.26.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: 6f560f34701b0230cbffc59837009874 NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: RELEASE_3_15 git_last_commit: 3f46a94 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NanoStringDiff_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringDiff_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringDiff_1.26.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R suggestsMe: NanoTube dependencyCount: 8 Package: NanoStringNCTools Version: 1.4.0 Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2 Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes, grDevices, IRanges, methods, pheatmap, RColorBrewer, stats, utils Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf License: MIT Archs: x64 MD5sum: 537807fcafd2acbddc25f9487c6ca99a NeedsCompilation: no Title: NanoString nCounter Tools Description: Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq Author: Patrick Aboyoun [aut], Nicole Ortogero [cre], Zhi Yang [ctb] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: RELEASE_3_15 git_last_commit: 2627586 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NanoStringNCTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringNCTools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringNCTools_1.4.0.tgz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools, GeoMxWorkflows importsMe: GeoDiff dependencyCount: 69 Package: NanoStringQCPro Version: 1.28.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: a25a6eff7798b35e9a53ea0ce487b125 NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringQCPro git_branch: RELEASE_3_15 git_last_commit: caedca9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NanoStringQCPro_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringQCPro_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringQCPro_1.28.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 94 Package: nanotatoR Version: 1.12.0 Depends: R (>= 4.1), Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, rlang, stringr, knitr, testthat, utils, AnnotationDbi, httr, GenomicRanges, tidyverse, VarfromPDB, org.Hs.eg.db, curl, dplyr, XML, XML2R Suggests: rmarkdown, yaml License: file LICENSE MD5sum: ccc0afc334d51f3e5e49d271e400a310 NeedsCompilation: no Title: Next generation structural variant annotation and classification Description: Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions (INDELs) and, more recently, small copy number variants (CNVs). However, due to utilization of short reads, it is not well suited for identification of structural variants (SV). Optical mapping (OM) from Bionano Genomics, utilizes long fluorescently labeled megabase size DNA molecules for de novo genome assembly and identification of SVs with a much higher sensitivity than WGS. Nevertheless, currently available SV annotation tools have limited number of functions. NanotatoR is an R package written to provide a set of annotations for SVs identified by OM. It uses Database of Genomic Variants (DGV), Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER) as well as a subset (154 samples) of 1000 Genome Project to calculate the population frequencies of the SVs (an optional internal cohort SV frequency calculation is also available). NanotatoR creates a primary gene list (PG) from NCBI databases based on proband’s phenotype specific keywords and compares the list to the set of genes overlapping/near SVs. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type (e.g., INDELs, Inversions, Translocations). Users then have a choice to filter SVs using the provided annotations for de novo (if parental samples are available) or inherited rare variants. biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation Author: Surajit Bhattacharya, Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/nanotatoR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/nanotatoR/issues git_url: https://git.bioconductor.org/packages/nanotatoR git_branch: RELEASE_3_15 git_last_commit: 5320b6b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nanotatoR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nanotatoR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nanotatoR_1.12.0.tgz vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html vignetteTitles: nanotatoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R dependencyCount: 147 Package: NanoTube Version: 1.2.0 Depends: R (>= 4.1), Biobase, ggplot2 Imports: fgsea, limma, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, qusage, shiny, testthat, xlsx License: GPL-3 MD5sum: 4b165e961d2841b663cd5003cb6e7a5c NeedsCompilation: no Title: An Easy Pipeline for NanoString nCounter Data Analysis Description: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages. biocViews: Software, GeneExpression, DifferentialExpression, QualityControl Author: Caleb Class [cre, aut] () Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: RELEASE_3_15 git_last_commit: fff7cac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NanoTube_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoTube_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoTube_1.2.0.tgz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 55 Package: NBAMSeq Version: 1.12.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: dcf6f638c46ffc95344f627ea6518853 NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: RELEASE_3_15 git_last_commit: 5e1d641 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NBAMSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NBAMSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NBAMSeq_1.12.0.tgz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 93 Package: NBSplice Version: 1.14.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics, BiocStyle, rmarkdown, markdown License: GPL (>=2) MD5sum: 7a86bb26a314c4e9850526ff1fd55611 NeedsCompilation: no Title: Negative Binomial Models to detect Differential Splicing Description: The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression. biocViews: Software, StatisticalMethod, AlternativeSplicing, Regression, DifferentialExpression, DifferentialSplicing, RNASeq, ImmunoOncology Author: Gabriela A. Merino and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NBSplice git_branch: RELEASE_3_15 git_last_commit: c153e60 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NBSplice_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NBSplice_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NBSplice_1.14.0.tgz vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html vignetteTitles: NBSplice-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R dependencyCount: 102 Package: ncdfFlow Version: 2.42.1 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: file LICENSE MD5sum: 8a85d6a4bf3b9e81d12b7b9cdf9b7a97 NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_15 git_last_commit: 1efced4 git_last_commit_date: 2022-06-30 Date/Publication: 2022-07-03 source.ver: src/contrib/ncdfFlow_2.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncdfFlow_2.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ncdfFlow_2.42.1.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace suggestsMe: COMPASS, cydar dependencyCount: 19 Package: ncGTW Version: 1.10.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: dc82adac85fee9f8f6d5759d17a6d50a NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: RELEASE_3_15 git_last_commit: bb83645 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ncGTW_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncGTW_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ncGTW_1.10.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 92 Package: NCIgraph Version: 1.44.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 68e26d090dc55c273198f990bc095eaf NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: RELEASE_3_15 git_last_commit: 745b6db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NCIgraph_1.44.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 52 Package: ncRNAtools Version: 1.6.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: 027431053e0d917cfed4762e19082f5c NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (), Rafael Ayala [aut] (), Guy-Bart Stan [aut] (), Rodrigo Ledesma-Amaro [aut] () Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: RELEASE_3_15 git_last_commit: 87809c2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ncRNAtools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncRNAtools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ncRNAtools_1.6.0.tgz vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R dependencyCount: 56 Package: ndexr Version: 1.18.0 Depends: igraph, RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause Archs: x64 MD5sum: 6e71f4c93915b9745afc00c373f264f0 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer , Frank Kramer , Alex Ishkin , Dexter Pratt Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: RELEASE_3_15 git_last_commit: 58fbbb4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ndexr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ndexr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ndexr_1.18.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R suggestsMe: netgsa dependencyCount: 40 Package: nearBynding Version: 1.6.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: dc87a82fc3bcc63c8cc38f61bc133e1b NeedsCompilation: no Title: Discern RNA structure proximal to protein binding Description: Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats. biocViews: Visualization, MotifDiscovery, DataRepresentation, StructuralPrediction, Clustering, MultipleComparison Author: Veronica Busa [cre] Maintainer: Veronica Busa SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.22), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: RELEASE_3_15 git_last_commit: 951bb7c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nearBynding_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nearBynding_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nearBynding_1.6.0.tgz vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf vignetteTitles: nearBynding Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R dependencyCount: 124 Package: Nebulosa Version: 1.6.0 Depends: R (>= 4.0), ggplot2, patchwork Imports: Seurat, SingleCellExperiment, SummarizedExperiment, ks, Matrix, stats, methods Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, SeuratObject License: GPL-3 MD5sum: 7d43e5bd95829152f9cd3d35a9c95bca NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] () Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: RELEASE_3_15 git_last_commit: 3d5561f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Nebulosa_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Nebulosa_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Nebulosa_1.6.0.tgz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R suggestsMe: SCpubr dependencyCount: 170 Package: NeighborNet Version: 1.14.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 MD5sum: 52a3e793861bb547f3db2212b76dec04 NeedsCompilation: no Title: Neighbor_net analysis Description: Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype. biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork Author: Sahar Ansari and Sorin Draghici Maintainer: Sahar Ansari git_url: https://git.bioconductor.org/packages/NeighborNet git_branch: RELEASE_3_15 git_last_commit: 80e94c3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NeighborNet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NeighborNet_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NeighborNet_1.14.0.tgz vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf vignetteTitles: NeighborNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R dependencyCount: 7 Package: nempi Version: 1.4.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit License: GPL-3 MD5sum: b2ab28be46a1a09a5f9ebcafdf173971 NeedsCompilation: no Title: Inferring unobserved perturbations from gene expression data Description: Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models. biocViews: Software, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneSignaling, Pathways, Network, Classification, NeuralNetwork, NetworkInference, ATACSeq, DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell, SystemsBiology Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/nempi/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/nempi/issues git_url: https://git.bioconductor.org/packages/nempi git_branch: RELEASE_3_15 git_last_commit: e412174 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nempi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nempi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nempi_1.4.0.tgz vignettes: vignettes/nempi/inst/doc/nempi.html vignetteTitles: nempi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nempi/inst/doc/nempi.R dependencyCount: 110 Package: netbiov Version: 1.30.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: a9dc2844e8734cc787a7469e20d6ec8e NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com git_url: https://git.bioconductor.org/packages/netbiov git_branch: RELEASE_3_15 git_last_commit: fb3dfb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netbiov_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netbiov_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netbiov_1.30.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R dependencyCount: 12 Package: netboost Version: 2.4.1 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, markdown, rmarkdown License: GPL-3 OS_type: unix MD5sum: 37e6f3e4f065db1c1c41bd65e7c94fd8 NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: https://github.com/PascalSchlosser/netboost/issues git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_15 git_last_commit: 4a62e24 git_last_commit_date: 2022-06-03 Date/Publication: 2022-06-05 source.ver: src/contrib/netboost_2.4.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/netboost_2.4.1.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 115 Package: netboxr Version: 1.7.1 Depends: R (>= 4.0.0), igraph (>= 1.2.4.1), parallel Imports: RColorBrewer, DT, stats, clusterProfiler, data.table, gplots, jsonlite, plyr Suggests: paxtoolsr, BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, cgdsr License: LGPL-3 + file LICENSE MD5sum: 36a728b7f86124efe44b3e756c702173 NeedsCompilation: no Title: netboxr Description: NetBox is a network-based approach that combines prior knowledge with a network clustering algorithm. The algorithm allows for the identification of functional modules and allows for combining multiple data types, such as mutations and copy number alterations. NetBox performs network analysis on human interaction networks, and comes pre-loaded with a Human Interaction Network (HIN) derived from four literature curated data sources, including the Human Protein Reference Database (HPRD), Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map. biocViews: Software,Network,Pathways,GraphAndNetwork,Reactome, SystemsBiology, GeneSetEnrichment, NetworkEnrichment, KEGG Author: Eric Minwei Liu [aut,cre], Augustin Luna [aut], Ethan Cerami [aut], Chris Sander [aut] Maintainer: Eirc Minwei Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netboxr git_branch: master git_last_commit: 4ec8615 git_last_commit_date: 2022-01-19 Date/Publication: 2022-01-27 source.ver: src/contrib/netboxr_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/netboxr_1.7.1.zip mac.binary.ver: bin/macosx/contrib/4.2/netboxr_1.7.1.tgz vignettes: vignettes/netboxr/inst/doc/netboxrTutorial.html vignetteTitles: NetBoxR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netboxr/inst/doc/netboxrTutorial.R dependencyCount: 140 Package: netDx Version: 1.8.0 Depends: R (>= 3.6) Imports: ROCR,pracma,ggplot2,glmnet,igraph,reshape2, parallel,stats,utils,MultiAssayExperiment,graphics,grDevices, methods,BiocFileCache,GenomicRanges, bigmemory,doParallel,foreach, combinat,rappdirs,GenomeInfoDb,S4Vectors, IRanges,RColorBrewer,Rtsne,httr,plotrix Suggests: curatedTCGAData, rmarkdown, testthat, knitr, BiocStyle, RCy3, clusterExperiment, netSmooth, scater License: MIT + file LICENSE MD5sum: 463c92539dd332552e1f1554cdefdee4 NeedsCompilation: no Title: Network-based patient classifier Description: netDx is a general-purpose algorithm to build a patient classifier from heterogenous patient data. The method converts data into patient similarity networks at the level of features. Feature selection identifies features of predictive value to each class. Methods are provided for versatile predictor design and performance evaluation using standard measures. netDx natively groups molecular data into pathway-level features and connects with Cytoscape for network visualization of pathway themes. For method details see: Pai et al. (2019). netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology. 15, e8497 biocViews: Classification, BiomedicalInformatics, Network, SystemsBiology Author: Shraddha Pai [aut, cre] (), Philipp Weber [aut], Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Anne Nøhr [ctb], Indy Ng [ctb], Ruth Isserlin [aut], Hussam Kaka [aut], Gary Bader [aut] Maintainer: Shraddha Pai URL: http://netdx.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netDx git_branch: RELEASE_3_15 git_last_commit: 094757e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netDx_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/netDx_1.7.1.tgz vignettes: vignettes/netDx/inst/doc/RawDataConversion.html, vignettes/netDx/inst/doc/ThreeWayClassifier.html vignetteTitles: 02. Running netDx with data in table format, 01. Build & test classifier with clinical and multi-omic data & pathway features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netDx/inst/doc/RawDataConversion.R, vignettes/netDx/inst/doc/ThreeWayClassifier.R dependencyCount: 110 Package: nethet Version: 1.28.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 MD5sum: d976c03b19c3defe7af59bad551f5e9d NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: RELEASE_3_15 git_last_commit: 1c63402 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nethet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nethet_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nethet_1.28.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 74 Package: netOmics Version: 1.2.0 Depends: R (>= 4.1) Imports: dplyr, ggplot2, igraph, magrittr, minet, purrr, tibble, tidyr, AnnotationDbi, GO.db, RandomWalkRestartMH, gprofiler2, methods, stats Suggests: mixOmics, timeOmics, tidyverse, BiocStyle, testthat, covr, rmarkdown, knitr License: GPL-3 MD5sum: 38488a3e285312e8890eec66e3b0b3a7 NeedsCompilation: no Title: Multi-Omics (time-course) network-based integration and interpretation Description: netOmics is a multi-omics networks builder and explorer. It uses a combination of network inference algorithms and and knowledge-based graphs to build multi-layered networks. The package can be combined with timeOmics to incorporate time-course expression data and build sub-networks from multi-omics kinetic clusters. Finally, from the generated multi-omics networks, propagation analyses allow the identification of missing biological functions (1), multi-omics mechanisms (2) and molecules between kinetic clusters (3). This helps to resolve complex regulatory mechanisms. biocViews: GraphAndNetwork, Software, TimeCourse, WorkflowStep, SystemsBiology, NetworkInference, Network Author: Antoine Bodein [aut, cre] Maintainer: Antoine Bodein URL: https://github.com/abodein/netOmics VignetteBuilder: knitr BugReports: https://github.com/abodein/netOmics/issues git_url: https://git.bioconductor.org/packages/netOmics git_branch: RELEASE_3_15 git_last_commit: 84fc38f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netOmics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netOmics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netOmics_1.2.0.tgz vignettes: vignettes/netOmics/inst/doc/netOmics.html vignetteTitles: netOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netOmics/inst/doc/netOmics.R dependencyCount: 110 Package: NetPathMiner Version: 1.32.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: ce1f2ea6d06986d3fc3581de16330afa NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_15 git_last_commit: 42c5764 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NetPathMiner_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NetPathMiner_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NetPathMiner_1.32.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 12 Package: netprioR Version: 1.22.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: 6dc555a8cf3d0185b21e94fa20aeec9a NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: RELEASE_3_15 git_last_commit: 32be07b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netprioR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netprioR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netprioR_1.22.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 49 Package: netresponse Version: 1.56.1 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) Archs: x64 MD5sum: e099cca2df097c45fae205cce7cae81b NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: RELEASE_3_15 git_last_commit: d6460b7 git_last_commit_date: 2022-09-03 Date/Publication: 2022-09-06 source.ver: src/contrib/netresponse_1.56.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/netresponse_1.56.1.zip mac.binary.ver: bin/macosx/contrib/4.2/netresponse_1.56.1.tgz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 73 Package: NetSAM Version: 1.36.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 0.6-1), tools (>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0) Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10), foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0), R2HTML (>= 2.2.0), DBI (>= 0.5-1) Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr, markdown License: LGPL MD5sum: 7f37a033357b620ee16ed1a0f1098452 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules. Author: Jing Wang Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: RELEASE_3_15 git_last_commit: 2e11a02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NetSAM_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/NetSAM_1.36.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 134 Package: netSmooth Version: 1.16.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 MD5sum: 7fac3e4e0e31890d851ada7212582f7c NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_15 git_last_commit: 91221f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netSmooth_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netSmooth_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netSmooth_1.16.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R suggestsMe: netDx dependencyCount: 169 Package: networkBMA Version: 2.35.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) MD5sum: d2d96b4e6caabd7ca8c76bcc09d8ce16 NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung SystemRequirements: liblapack-dev git_url: https://git.bioconductor.org/packages/networkBMA git_branch: master git_last_commit: a16970a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/networkBMA_2.35.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/networkBMA_2.35.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R suggestsMe: DREAM4 dependencyCount: 23 Package: netZooR Version: 1.0.0 Depends: R (>= 4.1.0), igraph, reticulate, pandaR, yarn Imports: RCy3, viridisLite, STRINGdb, Biobase, GOstats, AnnotationDbi, matrixStats, GO.db, org.Hs.eg.db, Matrix, gplots, nnet, data.table, vegan, stats, utils, reshape, reshape2, penalized, parallel, doParallel, foreach, ggplot2, ggdendro, grid, MASS, assertthat, tidyr, methods, dplyr, graphics Suggests: testthat (>= 2.1.0), knitr, rmarkdown, pkgdown License: GPL-3 Archs: x64 MD5sum: 3298111689749f553d5480dbbc07b870 NeedsCompilation: no Title: Integrate methods: PANDA, LIONESS, CONDOR, ALPACA, SAMBAR, MONSTER, OTTER, EGRET, and YARN into one workflow Description: PANDA(Passing Attributes between Networks for Data Assimilation) is a message-passing model to reconstruction gene regulatory network. It integrates multiple sources of biological data, including protein-protein interaction data, gene expression data, and sequence motif information to reconstruct genome-wide, condition-specific regulatory networks.[(Glass et al. 2013)]. LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks by applying linear interpolation to the predictions made by existing aggregate network inference approaches. CONDOR(COmplex Network Description Of Regulators)is a bipartite community structure analysis tool of biological networks, especially eQTL networks, including a method for scoring nodes based on their modularity contribution.[(Platig et al. 2016). ALPACA(ALtered Partitions Across Community Architectures) is a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules.[(Padi and Quackenbush 2018)]. This package integrates pypanda--the Python implementation of PANDA and LIONESS(https://github.com/davidvi/pypanda),the R implementation of CONDOR(https://github.com/jplatig/condor) and the R implementation of ALPACA (https://github.com/meghapadi/ALPACA) into one workflow. Each tool can be call in this package by one function, and the relevant output could be accessible in current R session for downstream analysis. biocViews: NetworkInference, Network, GeneRegulation, GeneExpression, Transcription, Microarray, GraphAndNetwork Author: Tian Wang [aut], Marouen Ben Guebila [aut, cre], John Platig [aut], Marieke Kuijjer [aut], Magha Padi [aut], Rebekka Burkholz [aut], Deborah Weighill [aut] Maintainer: Marouen Ben Guebila VignetteBuilder: knitr BugReports: https://github.com/netZoo/netZooR/issues git_url: https://git.bioconductor.org/packages/netZooR git_branch: RELEASE_3_15 git_last_commit: 9f0e4a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/netZooR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netZooR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netZooR_1.0.0.tgz vignettes: vignettes/netZooR/inst/doc/CONDOR.html vignetteTitles: CONDOR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netZooR/inst/doc/CONDOR.R dependencyCount: 207 Package: NeuCA Version: 1.2.0 Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment Suggests: BiocStyle, knitr, rmarkdown, networkD3 License: GPL-2 MD5sum: 79c8853c649e7818061d508595a81bbd NeedsCompilation: no Title: NEUral network-based single-Cell Annotation tool Description: NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types. biocViews: SingleCell, Software, Classification, NeuralNetwork, RNASeq, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Ziyi Li [aut], Hao Feng [aut, cre] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NeuCA git_branch: RELEASE_3_15 git_last_commit: 227a457 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NeuCA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NeuCA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NeuCA_1.2.0.tgz vignettes: vignettes/NeuCA/inst/doc/NeuCA.html vignetteTitles: NeuCA Package User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeuCA/inst/doc/NeuCA.R dependencyCount: 63 Package: NewWave Version: 1.6.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 MD5sum: 9a7aa9ee04cacce9fb8ae342737e88ed NeedsCompilation: no Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: RELEASE_3_15 git_last_commit: e1f4d30 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NewWave_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NewWave_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NewWave_1.6.0.tgz vignettes: vignettes/NewWave/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NewWave/inst/doc/vignette.R dependencyCount: 42 Package: ngsReports Version: 1.12.4 Depends: R (>= 4.1.0), BiocGenerics, ggplot2 (>= 3.3.5), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.0.0), DT, forcats, ggdendro, grDevices (>= 3.6.0), grid, lifecycle, lubridate, methods, pander, plotly (>= 4.9.4), readr, reshape2, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, knitr, testthat, truncnorm License: file LICENSE MD5sum: 99808342a226732e909c61ecfb2e4535 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects. biocViews: QualityControl, ReportWriting Author: Steve Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Steve Pederson URL: https://github.com/steveped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/steveped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_15 git_last_commit: 46f3aa2 git_last_commit_date: 2022-07-20 Date/Publication: 2022-07-24 source.ver: src/contrib/ngsReports_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/ngsReports_1.12.4.zip mac.binary.ver: bin/macosx/contrib/4.2/ngsReports_1.12.4.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 109 Package: nnNorm Version: 2.60.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 4a82d6f10419e37a7b0d7b6fe8a83756 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: RELEASE_3_15 git_last_commit: 7b10a62 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nnNorm_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nnNorm_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nnNorm_2.60.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 8 Package: nnSVG Version: 1.0.4 Depends: R (>= 4.2) Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats Suggests: BiocStyle, knitr, rmarkdown, STexampleData, scran, ggplot2, testthat License: MIT + file LICENSE MD5sum: e91031c00aa67c024c2268f8ef5879ed NeedsCompilation: no Title: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data Description: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Lukas M. Weber [aut, cre] (), Stephanie C. Hicks [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues git_url: https://git.bioconductor.org/packages/nnSVG git_branch: RELEASE_3_15 git_last_commit: ef8699b git_last_commit_date: 2022-07-18 Date/Publication: 2022-07-19 source.ver: src/contrib/nnSVG_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/nnSVG_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/nnSVG_1.0.4.tgz vignettes: vignettes/nnSVG/inst/doc/nnSVG.html vignetteTitles: nnSVG tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R dependencyCount: 99 Package: NOISeq Version: 2.40.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 951bee5738bffe47f6331b69ac5375c3 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: RELEASE_3_15 git_last_commit: 364281c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NOISeq_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NOISeq_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NOISeq_2.40.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: benchdamic, CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR, GeoTcgaData dependencyCount: 11 Package: nondetects Version: 2.26.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: 3f5f46f3a8ddcdb5b361f981003da096 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_15 git_last_commit: 064534c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nondetects_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nondetects_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nondetects_2.26.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 70 Package: NoRCE Version: 1.8.0 Depends: R (>= 4.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db,zlibbioc, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods,markdown License: MIT + file LICENSE MD5sum: b683659286fc7a570fd89c9799ef3055 NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: RELEASE_3_15 git_last_commit: ffeef02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NoRCE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NoRCE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NoRCE_1.8.0.tgz vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 124 Package: normalize450K Version: 1.24.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 2739c9e1439a4fc48ecf183465fc71bf NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: RELEASE_3_15 git_last_commit: b25b2b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/normalize450K_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/normalize450K_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/normalize450K_1.24.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 12 Package: NormalyzerDE Version: 1.14.0 Depends: R (>= 3.6) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 095f493e35c9a97f7016d769537b4919 NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: RELEASE_3_15 git_last_commit: b459349 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NormalyzerDE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NormalyzerDE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NormalyzerDE_1.14.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R dependencyCount: 158 Package: NormqPCR Version: 1.42.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 63c6a7df35fdd265d46febe4df25e1fc NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: RELEASE_3_15 git_last_commit: 0e52b89 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NormqPCR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NormqPCR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NormqPCR_1.42.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 37 Package: normr Version: 1.22.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 MD5sum: 4faa743d2aa7cca565a4f9e74f19d201 NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: RELEASE_3_15 git_last_commit: b5d82ba git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/normr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/normr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/normr_1.22.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 80 Package: NPARC Version: 1.8.0 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 MD5sum: 7667a9a42d75294ba663d0129fef2115 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: RELEASE_3_15 git_last_commit: 97ade37 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NPARC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NPARC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NPARC_1.8.0.tgz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 57 Package: npGSEA Version: 1.32.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 02d9c6eef849c5b5f6023b500a291f2d NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: RELEASE_3_15 git_last_commit: f7376a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/npGSEA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/npGSEA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/npGSEA_1.32.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 50 Package: NTW Version: 1.46.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: a6b4982d2c52ab8aabde69cf86400308 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: RELEASE_3_15 git_last_commit: a57481a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NTW_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NTW_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NTW_1.46.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 4 Package: nucleoSim Version: 1.24.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 8842d3d7c109d3b9efe7ae9e00e82394 NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: RELEASE_3_15 git_last_commit: 13eeb5d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nucleoSim_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nucleoSim_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nucleoSim_1.24.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 8 Package: nucleR Version: 2.28.0 Depends: R (>= 3.5.0), methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: 4b1696df25c407d30a2b5a1b4c5b79c2 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Alba Sala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_15 git_last_commit: fbb1dd6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nucleR_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nucleR_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nucleR_2.28.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 79 Package: nuCpos Version: 1.14.0 Depends: R (>= 3.6) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: file LICENSE MD5sum: 7303768fcfbc3644cae0ad51d5373660 NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Furthermore, effect of genetic alterations on nucleosome occupancy can be predicted with this package. The parental package NuPoP, which is based on an MNase-seq-based map of budding yeast nucleosomes, was developed by Ji-Ping Wang and Liqun Xi, licensed under GPL-2. biocViews: Genetics, Epigenetics, NucleosomePositioning, HiddenMarkovModel, ImmunoOncology Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_15 git_last_commit: fed8f9d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nuCpos_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nuCpos_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nuCpos_1.14.0.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: nullranges Version: 1.2.0 Imports: stats, IRanges, GenomicRanges, GenomeInfoDb, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, ks, speedglm, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, DNAcopy, RcppHMM, AnnotationHub, nullrangesData, excluderanges, ensembldb, EnsDb.Hsapiens.v86, microbenchmark, patchwork, plotgardener, magrittr, tidyr, cobalt License: GPL-3 MD5sum: b4f66db40392fce0694b946eb520f573 NeedsCompilation: no Title: Generation of null ranges via bootstrapping or covariate matching Description: Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package. biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics, Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation, Annotation, GenomeWideAssociation, HistoneModification, ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel Author: Michael Love [aut, cre] (), Wancen Mu [aut] (), Eric Davis [aut] (), Douglas Phanstiel [aut] (), Stuart Lee [aut] (), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love URL: https://nullranges.github.io/nullranges VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: RELEASE_3_15 git_last_commit: c465f9f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/nullranges_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nullranges_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nullranges_1.2.0.tgz vignettes: vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_ranges.html, vignettes/nullranges/inst/doc/nullranges.html, vignettes/nullranges/inst/doc/segmented_boot_ranges.html, vignettes/nullranges/inst/doc/unseg_boot_ranges.html vignetteTitles: 3. Case study II: CTCF orientation, 2. Case study I: CTCF occupancy, 1. Overview of matchRanges, 0. Introduction to nullranges, 4. Segmented block bootstrap, 5. Unsegmented block bootstrap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_ranges.R, vignettes/nullranges/inst/doc/nullranges.R, vignettes/nullranges/inst/doc/segmented_boot_ranges.R, vignettes/nullranges/inst/doc/unseg_boot_ranges.R dependencyCount: 97 Package: NuPoP Version: 2.4.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 17baaf7460f4095184dcf3891ef0bf24 NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification,NucleosomePositioning, HiddenMarkovModel Author: Ji-Ping Wang ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: RELEASE_3_15 git_last_commit: 08a1f73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NuPoP_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NuPoP_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NuPoP_2.4.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP.html vignetteTitles: NuPoP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R suggestsMe: nuCpos dependencyCount: 2 Package: NxtIRFcore Version: 1.2.1 Depends: R (>= 3.5.0), NxtIRFdata Imports: methods, stats, utils, tools, parallel, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, IRanges, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, zlibbioc, RcppProgress Suggests: knitr, rmarkdown, pheatmap, shiny, openssl, crayon, egg, DESeq2, limma, DoubleExpSeq, Rsubread, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 3d97b955958a9003303cb643b065f620 NeedsCompilation: yes Title: Core Engine for NxtIRF: a User-Friendly Intron Retention and Alternative Splicing Analysis using the IRFinder Engine Description: Interactively analyses Intron Retention and Alternative Splicing Events (ASE) in RNA-seq data. NxtIRF quantifies ASE events in BAM files aligned to the genome using a splice-aware aligner such as STAR. The core quantitation algorithm relies on the IRFinder/C++ engine ported via Rcpp for multi-platform compatibility. In addition, NxtIRF provides convenient pipelines for downstream analysis and publication-ready visualisation tools. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing Author: Alex Chit Hei Wong [aut, cre, cph], William Ritchie [aut, cph], Ulf Schmitz [ctb] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/NxtIRFcore SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/NxtIRFcore git_branch: RELEASE_3_15 git_last_commit: b13e810 git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/NxtIRFcore_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/NxtIRFcore_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/NxtIRFcore_1.2.1.tgz vignettes: vignettes/NxtIRFcore/inst/doc/NxtIRF.html vignetteTitles: NxtIRFcore: Differential Alternative Splicing and Intron Retention analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NxtIRFcore/inst/doc/NxtIRF.R dependencyCount: 147 Package: occugene Version: 1.56.0 Depends: R (>= 2.0.0) License: GPL (>= 2) Archs: x64 MD5sum: 0137d41a15935d85d68d6f289a93fd6a NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: RELEASE_3_15 git_last_commit: 76a934c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/occugene_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/occugene_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/occugene_1.56.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.70.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL Archs: x64 MD5sum: dfec6d9711f126a36782eb2504569ace NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: RELEASE_3_15 git_last_commit: 6fc43d9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OCplus_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OCplus_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OCplus_1.70.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 17 Package: ODER Version: 1.2.0 Depends: R (>= 4.1) Imports: BiocGenerics, BiocFileCache, dasper, derfinder, dplyr, IRanges, GenomeInfoDb, GenomicRanges, ggplot2, ggpubr, ggrepel, magrittr, rtracklayer, S4Vectors, stringr, data.table, megadepth, methods, plyr, purrr, tibble, utils Suggests: BiocStyle, covr, knitr, recount, RefManageR, rmarkdown, sessioninfo, SummarizedExperiment, testthat (>= 3.0.0), GenomicFeatures, xfun License: Artistic-2.0 MD5sum: b70000b3486b0137221f407db7d2f478 NeedsCompilation: no Title: Optimising the Definition of Expressed Regions Description: The aim of ODER is to identify previously unannotated expressed regions (ERs) using RNA-sequencing data. For this purpose, ODER defines and optimises the definition of ERs, then connected these ERs to genes using junction data. In this way, ODER improves gene annotation. Gene annotation is a staple input of many bioinformatic pipelines and a more complete gene annotation can enable more accurate interpretation of disease associated variants. biocViews: Software, GenomeAnnotation, Transcriptomics, RNASeq, GeneExpression, Sequencing, DataImport Author: Emmanuel Olagbaju [aut], David Zhang [aut, cre] (), Sebastian Guelfi [ctb], Siddharth Sethi [ctb] Maintainer: David Zhang URL: https://github.com/eolagbaju/ODER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/ODER git_url: https://git.bioconductor.org/packages/ODER git_branch: RELEASE_3_15 git_last_commit: 933b5a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ODER_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ODER_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ODER_1.2.0.tgz vignettes: vignettes/ODER/inst/doc/ODER_overview.html vignetteTitles: Introduction to ODER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ODER/inst/doc/ODER_overview.R dependencyCount: 215 Package: odseq Version: 1.24.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: ca7c77b5686d347a6efc955cf31f80f4 NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: RELEASE_3_15 git_last_commit: 203059b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/odseq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/odseq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/odseq_1.24.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 32 Package: OGRE Version: 1.0.0 Depends: R (>= 4.1.0), S4Vectors Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz, IRanges, AnnotationHub, grDevices, stats, GenomeInfoDb, shiny, shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) License: Artistic-2.0 MD5sum: 2947a6a27adf2986d502efada89110ff NeedsCompilation: no Title: Calculate, visualize and analyse overlap between genomic regions Description: OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level. biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation, Metagenomics, Visualization, Sequencing Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb], Sarah Sandmann [ctb], Sandra Laurentino [ctb] Maintainer: Sven Berres URL: https://github.com/svenbioinf/OGRE/ VignetteBuilder: knitr BugReports: https://github.com/svenbioinf/OGRE/issues git_url: https://git.bioconductor.org/packages/OGRE git_branch: RELEASE_3_15 git_last_commit: 5f9a0d1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OGRE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OGRE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OGRE_1.0.0.tgz vignettes: vignettes/OGRE/inst/doc/OGRE.html vignetteTitles: OGRE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGRE/inst/doc/OGRE.R dependencyCount: 169 Package: oligo Version: 1.60.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) MD5sum: 37ea8a64cab34e932498218ce680116b NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: RELEASE_3_15 git_last_commit: d667332 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oligo_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oligo_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oligo_1.60.0.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd importsMe: ArrayExpress, cn.farms, crossmeta, frma, ITALICS, mimager suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 52 Package: oligoClasses Version: 1.58.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 2df4ab1b3ac3d63b4afd5c8cd6a987d4 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: RELEASE_3_15 git_last_commit: 5544e93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oligoClasses_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oligoClasses_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oligoClasses_1.58.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, maEndToEnd importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 48 Package: OLIN Version: 1.74.1 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 Archs: x64 MD5sum: 1d52cfd31ba9fae4b0fda16f480057a9 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: RELEASE_3_15 git_last_commit: c74737b git_last_commit_date: 2022-05-25 Date/Publication: 2022-05-26 source.ver: src/contrib/OLIN_1.74.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/OLIN_1.74.1.zip mac.binary.ver: bin/macosx/contrib/4.2/OLIN_1.74.1.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack dependencyCount: 10 Package: OLINgui Version: 1.70.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 7afda9aac43891002b09f5061027f1f8 NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: RELEASE_3_15 git_last_commit: d3f94bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OLINgui_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OLINgui_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OLINgui_1.70.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 16 Package: OmaDB Version: 2.12.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2112996f621a885989d18ada91d0e977 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: RELEASE_3_15 git_last_commit: 1c7747a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OmaDB_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmaDB_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmaDB_2.12.0.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R importsMe: PhyloProfile dependencyCount: 57 Package: omicade4 Version: 1.36.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: 3367f78a18d414193eef854cd3f6c29e NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: RELEASE_3_15 git_last_commit: d8d5a55 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/omicade4_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicade4_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicade4_1.36.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, ropls dependencyCount: 36 Package: OmicCircos Version: 1.34.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: b9be801cbd671de265279eb31075f1e1 NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: RELEASE_3_15 git_last_commit: 6d746e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OmicCircos_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmicCircos_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmicCircos_1.34.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 16 Package: omicplotR Version: 1.16.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: 345cdc2e35665cdba60afc504f3df28a NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_15 git_last_commit: f99019b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/omicplotR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicplotR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicplotR_1.16.0.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 100 Package: omicRexposome Version: 1.18.0 Depends: R (>= 3.5.0), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: d6d0c07e3ff0ac3d47149a36ee873730 NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: RELEASE_3_15 git_last_commit: 2694d60 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/omicRexposome_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicRexposome_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicRexposome_1.18.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 214 Package: OmicsLonDA Version: 1.12.0 Depends: R(>= 3.6) Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2, BiocParallel, parallel, grDevices, graphics, stats, utils, methods, BiocGenerics Suggests: knitr, rmarkdown, testthat, devtools, BiocManager License: MIT + file LICENSE MD5sum: 6178c8b6b26c902aac79b1daba047783 NeedsCompilation: no Title: Omics Longitudinal Differential Analysis Description: Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups. biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics, Lipidomics, Transcriptomics, Regression Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder Maintainer: Ahmed A. Metwally URL: https://github.com/aametwally/OmicsLonDA VignetteBuilder: knitr BugReports: https://github.com/aametwally/OmicsLonDA/issues git_url: https://git.bioconductor.org/packages/OmicsLonDA git_branch: RELEASE_3_15 git_last_commit: c0584ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OmicsLonDA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmicsLonDA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmicsLonDA_1.12.0.tgz vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html vignetteTitles: OmicsLonDA Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R dependencyCount: 67 Package: OMICsPCA Version: 1.14.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: bb41d23653bff2fb6f1dc80f4cf8170a NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: RELEASE_3_15 git_last_commit: 7077ea9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OMICsPCA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OMICsPCA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OMICsPCA_1.14.0.tgz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 223 Package: omicsPrint Version: 1.16.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: 3f29990f2d4c1328ba535a620d5db129 NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: RELEASE_3_15 git_last_commit: 66bc992 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/omicsPrint_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicsPrint_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicsPrint_1.16.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 48 Package: omicsViewer Version: 1.0.1 Depends: R (>= 4.2) Imports: survminer, survival, fastmatch, reshape2, stringr, beeswarm, grDevices, DT, shiny, shinythemes, shinyWidgets, plotly, networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea, openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics, grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2, S4Vectors, SummarizedExperiment, RSQLite, Matrix, shinycssloaders Suggests: BiocStyle, knitr, rmarkdown, unittest License: GPL-2 MD5sum: 3c005e6242121d733bf4a07656d2846c NeedsCompilation: no Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript. biocViews: Software, Visualization, GeneSetEnrichment, DifferentialExpression, MotifDiscovery, Network, NetworkEnrichment Author: Chen Meng [aut, cre] Maintainer: Chen Meng URL: https://github.com/mengchen18/omicsViewer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk BugReports: https://github.com/mengchen18/omicsViewer git_url: https://git.bioconductor.org/packages/omicsViewer git_branch: RELEASE_3_15 git_last_commit: 4651c9a git_last_commit_date: 2022-05-09 Date/Publication: 2022-05-15 source.ver: src/contrib/omicsViewer_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicsViewer_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/omicsViewer_1.0.1.tgz vignettes: vignettes/omicsViewer/inst/doc/quickStart.html vignetteTitles: quickStart.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R dependencyCount: 189 Package: Omixer Version: 1.6.0 Depends: R (>= 4.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: 69bb21625776bb9abce95edd2ef04a2b NeedsCompilation: no Title: Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies Description: Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: RELEASE_3_15 git_last_commit: 697fb1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Omixer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Omixer_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Omixer_1.6.0.tgz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 56 Package: OmnipathR Version: 3.4.7 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr, httr, igraph, jsonlite, later, logger, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown, stats, stringr, tibble, tidyr, tidyselect, tools, utils, withr, xml2, yaml Suggests: BiocStyle, bookdown, dnet, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, Rgraphviz, rmarkdown, smoof, supraHex, testthat License: MIT + file LICENSE MD5sum: 947925b322a42fb51763a0787812deed NeedsCompilation: no Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (), Denes Turei [cre, aut] (), Attila Gabor [aut] () Maintainer: Denes Turei URL: https://saezlab.github.io/OmnipathR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: RELEASE_3_15 git_last_commit: fd6742c git_last_commit_date: 2022-10-15 Date/Publication: 2022-10-16 source.ver: src/contrib/OmnipathR_3.4.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmnipathR_3.4.7.zip mac.binary.ver: bin/macosx/contrib/4.2/OmnipathR_3.4.7.tgz vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html, vignettes/OmnipathR/inst/doc/db_manager.html, vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/extra_attrs.html, vignettes/OmnipathR/inst/doc/nichenet.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html, vignettes/OmnipathR/inst/doc/paths.html vignetteTitles: OmniPath Bioconductor workshop, Database manager, Building networks around drug-targets using OmnipathR, Extra attributes, Using NicheNet with OmnipathR, OmnipathR: an R client for the OmniPath web service, Pathway construction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R, vignettes/OmnipathR/inst/doc/db_manager.R, vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/extra_attrs.R, vignettes/OmnipathR/inst/doc/nichenet.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R, vignettes/OmnipathR/inst/doc/paths.R importsMe: wppi suggestsMe: decoupleR dependencyCount: 76 Package: ompBAM Version: 1.0.0 Imports: utils, Rcpp, zlibbioc Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 4485a52ce72047a7d04fc9973cfef5fe NeedsCompilation: no Title: C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files Description: This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM. biocViews: Alignment, DataImport, RNASeq, Software, Sequencing, Transcriptomics, SingleCell Author: Alex Chit Hei Wong [aut, cre, cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/ompBAM VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/ompBAM git_branch: RELEASE_3_15 git_last_commit: e7dbec8 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/ompBAM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ompBAM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ompBAM_1.0.0.tgz vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html vignetteTitles: ompBAM API Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R dependencyCount: 4 Package: oncomix Version: 1.18.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: f88bca5e079ccf3dda3adfe0f48f9188 NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: RELEASE_3_15 git_last_commit: 28b510f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oncomix_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oncomix_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oncomix_1.18.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 56 Package: OncoScore Version: 1.24.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: bc524c8f64b7d41e2b0a7a501bcec5ce NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [aut] (), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [cre, aut] (), Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: RELEASE_3_15 git_last_commit: 16ac0fc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OncoScore_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OncoScore_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OncoScore_1.24.0.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/vignette.R dependencyCount: 71 Package: OncoSimulR Version: 3.4.0 Depends: R (>= 3.5.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, stringr LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: cc391e9e45ca4b1959bd21a58686c037 NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb], Niklas Endres [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: RELEASE_3_15 git_last_commit: 17fdf39 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OncoSimulR_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OncoSimulR_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OncoSimulR_3.4.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 97 Package: oneSENSE Version: 1.18.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) MD5sum: 35ec588a867c52885c0b8ef3e4d4a0c6 NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: ImmunoOncology, Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oneSENSE git_branch: RELEASE_3_15 git_last_commit: f5159ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oneSENSE_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/oneSENSE_1.18.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R dependencyCount: 101 Package: onlineFDR Version: 2.4.0 Imports: stats, Rcpp, RcppProgress, dplyr, tidyr, ggplot2, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: effd4226e6941e79322a28d614ee4b50 NeedsCompilation: yes Title: Online error control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive sequentially in a stream. In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb], Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: RELEASE_3_15 git_last_commit: 1e89166 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/onlineFDR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/onlineFDR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/onlineFDR_2.4.0.tgz vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html, vignettes/onlineFDR/inst/doc/onlineFDR.html, vignettes/onlineFDR/inst/doc/theory.html vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR package, The theory behind onlineFDR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R, vignettes/onlineFDR/inst/doc/onlineFDR.R, vignettes/onlineFDR/inst/doc/theory.R dependencyCount: 49 Package: ontoProc Version: 1.18.0 Depends: R (>= 3.5), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: bc7e208544d558ff11182a2122d16fb4 NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_15 git_last_commit: 6a28f6f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ontoProc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ontoProc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ontoProc_1.18.0.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R importsMe: pogos, tenXplore suggestsMe: BiocOncoTK, SingleRBook, scDiffCom dependencyCount: 95 Package: openCyto Version: 2.8.4 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,gtools,flowCore(>= 1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.99.1),flowStats(>= 3.99.1),flowClust(>= 3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML License: file LICENSE Archs: x64 MD5sum: 676ba598f0d412d4210d3288cdd6048c NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_15 git_last_commit: b3dc53c git_last_commit_date: 2022-07-10 Date/Publication: 2022-07-12 source.ver: src/contrib/openCyto_2.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/openCyto_2.8.4.zip mac.binary.ver: bin/macosx/contrib/4.2/openCyto_2.8.4.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime, flowWorkspace, ggcyto dependencyCount: 120 Package: openPrimeR Version: 1.18.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: 0defc08f2fb39a4bfc067fe88045deff NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: RELEASE_3_15 git_last_commit: fbd389f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/openPrimeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/openPrimeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/openPrimeR_1.18.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependsOnMe: openPrimeRui dependencyCount: 118 Package: openPrimeRui Version: 1.18.0 Depends: R (>= 4.0.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 Archs: x64 MD5sum: 7b6b104e57638b2785716d1a29076227 NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeRui git_branch: RELEASE_3_15 git_last_commit: 5dd3680 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/openPrimeRui_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/openPrimeRui_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/openPrimeRui_1.18.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R dependencyCount: 141 Package: OpenStats Version: 1.8.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: 6f501884943802f98c80b777bdebfd22 NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Hamed Haseli Mashhadi URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: RELEASE_3_15 git_last_commit: 740737a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OpenStats_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OpenStats_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OpenStats_1.8.0.tgz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 131 Package: oposSOM Version: 2.14.0 Depends: R (>= 4.0.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl LinkingTo: RcppParallel, Rcpp License: GPL (>=2) MD5sum: 1792700f1ab8f3a5c6952550bb20ee57 NeedsCompilation: yes Title: Comprehensive analysis of transcriptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: RELEASE_3_15 git_last_commit: 603a4c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oposSOM_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oposSOM_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oposSOM_2.14.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 85 Package: oppar Version: 1.24.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 MD5sum: 5f71527cd3189c19f73e3c173eac73a1 NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: RELEASE_3_15 git_last_commit: db58829 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oppar_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oppar_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oppar_1.24.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 80 Package: oppti Version: 1.10.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT Archs: x64 MD5sum: be3ad14dc46b97fe8ba72dc756ffc20e NeedsCompilation: no Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues git_url: https://git.bioconductor.org/packages/oppti git_branch: RELEASE_3_15 git_last_commit: 574b338 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/oppti_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oppti_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oppti_1.10.0.tgz vignettes: vignettes/oppti/inst/doc/analysis.html vignetteTitles: Outlier Protein and Phosphosite Target Identifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppti/inst/doc/analysis.R dependencyCount: 125 Package: optimalFlow Version: 1.8.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 Archs: x64 MD5sum: 8aefb77a09931c162a028b87ae4c922c NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: RELEASE_3_15 git_last_commit: 6d0ff33 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/optimalFlow_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/optimalFlow_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/optimalFlow_1.8.0.tgz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 88 Package: OPWeight Version: 1.18.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: 462b0c9ada352da76b9908c11f7300d5 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: RELEASE_3_15 git_last_commit: 31e3e89 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OPWeight_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OPWeight_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OPWeight_1.18.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 43 Package: OrderedList Version: 1.68.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: a6984c8b10554b2c312d75b34b4987e5 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: RELEASE_3_15 git_last_commit: 9422d38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OrderedList_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OrderedList_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OrderedList_1.68.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 9 Package: ORFhunteR Version: 1.4.0 Depends: Biostrings, rtracklayer, Peptides Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table, stringr, randomForest, xfun, stats, utils, parallel, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: dcf2ea9b1cf5614bbb55d8277f36631f NeedsCompilation: yes Title: Predict open reading frames in nucleotide sequences Description: The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Classification, FeatureExtraction Author: Vasily V. Grinev [aut, cre] (), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut], Petr V. Nazarov [aut] () Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: RELEASE_3_15 git_last_commit: ec884eb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ORFhunteR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ORFhunteR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ORFhunteR_1.4.0.tgz vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html vignetteTitles: The ORFhunteR package: User’s manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R dependencyCount: 56 Package: ORFik Version: 1.16.6 Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt, biomartr, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite, methods (>= 3.6.0), R.utils, Rcpp (>= 1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools, utils, xml2 (>= 1.2.0) LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: abc5a130f432dbbf66d098cc4f2bb3db NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Evind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: RELEASE_3_15 git_last_commit: 59b4114 git_last_commit_date: 2022-10-12 Date/Publication: 2022-10-13 source.ver: src/contrib/ORFik_1.16.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/ORFik_1.16.6.zip mac.binary.ver: bin/macosx/contrib/4.2/ORFik_1.16.6.tgz vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html vignetteTitles: Annotation_Alignment.html, ORFikExperiment.html, ORFik Overview, Ribo-seq_pipeline.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R dependsOnMe: RiboCrypt importsMe: TFHAZ dependencyCount: 139 Package: Organism.dplyr Version: 1.24.0 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils, BiocFileCache, DBI, dbplyr, tibble Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, BiocStyle, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: 77a3f38d1d882756270ca660d2533efe NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng [aut] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_15 git_last_commit: 663cc68 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Organism.dplyr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Organism.dplyr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Organism.dplyr_1.24.0.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 99 Package: OrganismDbi Version: 1.38.1 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4) Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 Archs: x64 MD5sum: d7676211728e7e82f0a78bf5dfbcc754 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_15 git_last_commit: fa8da4d git_last_commit_date: 2022-06-16 Date/Publication: 2022-06-16 source.ver: src/contrib/OrganismDbi_1.38.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/OrganismDbi_1.38.1.zip mac.binary.ver: bin/macosx/contrib/4.2/OrganismDbi_1.38.1.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, gpart, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 100 Package: orthogene Version: 1.2.1 Depends: R (>= 4.1) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, DelayedMatrixStats, Matrix.utils, grr, repmis, ggtree, tools Suggests: remotes, knitr, BiocStyle, covr, markdown, rmarkdown, here, testthat (>= 3.0.0), piggyback, badger, magick, desc, hrbrthemes, Cairo, yulab.utils, haven, GenomeInfoDbData, ape, phytools, rphylopic, TreeTools, RColorBrewer, ggimage License: GPL-3 MD5sum: 6593af692158a8543fb2fc90bf0dd329 NeedsCompilation: no Title: Interspecies gene mapping Description: orthogene is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date interspecies gene ortholog mappings across 700+ organisms. It also provides various utility functions to map common objects (e.g. data.frames, gene expression matrices, lists) onto 1:1 gene orthologs from any other species. biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics, Transcriptomics, GeneExpression Author: Brian Schilder [cre] () Maintainer: Brian Schilder URL: https://github.com/neurogenomics/orthogene VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/orthogene/issues git_url: https://git.bioconductor.org/packages/orthogene git_branch: RELEASE_3_15 git_last_commit: 1dc2ecb git_last_commit_date: 2022-10-02 Date/Publication: 2022-10-02 source.ver: src/contrib/orthogene_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/orthogene_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/orthogene_1.2.1.tgz vignettes: vignettes/orthogene/inst/doc/docker.html, vignettes/orthogene/inst/doc/infer_species.html, vignettes/orthogene/inst/doc/orthogene.html vignetteTitles: docker, Infer species, orthogene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/orthogene/inst/doc/docker.R, vignettes/orthogene/inst/doc/infer_species.R, vignettes/orthogene/inst/doc/orthogene.R importsMe: EWCE dependencyCount: 154 Package: OSAT Version: 1.44.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 8d15988db902c5f1802f37d23e10674c NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: RELEASE_3_15 git_last_commit: 135ebde git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OSAT_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OSAT_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OSAT_1.44.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R dependencyCount: 2 Package: Oscope Version: 1.26.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 7bfd0c7c2a9e7ff137b1d5883c5530a4 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: RELEASE_3_15 git_last_commit: 958bc31 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Oscope_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Oscope_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Oscope_1.26.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R dependencyCount: 57 Package: OTUbase Version: 1.46.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: b54730526688e1a3ea75c0b00a35d49c NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: RELEASE_3_15 git_last_commit: e7ff967 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OTUbase_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OTUbase_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OTUbase_1.46.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependencyCount: 56 Package: OUTRIDER Version: 1.14.0 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, Rcpp, reshape2, S4Vectors, scales, splines, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr License: MIT + file LICENSE MD5sum: b2ef00b37b162d695f4bf81ca0102be1 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut], Christian Mertes [aut, cre], Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_15 git_last_commit: b1aff8f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OUTRIDER_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OUTRIDER_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OUTRIDER_1.14.0.tgz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R importsMe: FRASER dependencyCount: 156 Package: OVESEG Version: 1.12.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 MD5sum: f3a1906529ba50e08000eaacaa1fd46b NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: RELEASE_3_15 git_last_commit: d9bae49 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/OVESEG_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OVESEG_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OVESEG_1.12.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 37 Package: PAA Version: 1.30.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 7d4ef939633f3383e1e16908b0fb39c8 NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: RELEASE_3_15 git_last_commit: 8e9673a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PAA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAA_1.30.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 83 Package: packFinder Version: 1.8.0 Depends: R (>= 4.0.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 Archs: x64 MD5sum: 236c9c50748a5c4fd6545c4b75cbfdb0 NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre] (), Marco Catoni [aut] () Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: RELEASE_3_15 git_last_commit: 68de9df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/packFinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/packFinder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/packFinder_1.8.0.tgz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 30 Package: padma Version: 1.6.0 Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot License: GPL (>=3) Archs: x64 MD5sum: 20ca4d8a638aaa6c48a87391fb7180a1 NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: RELEASE_3_15 git_last_commit: 53dd37a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/padma_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/padma_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/padma_1.6.0.tgz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 127 Package: PADOG Version: 1.38.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 9e09398f13e7125954fbc3bd1b21b5bb NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: RELEASE_3_15 git_last_commit: 99a6649 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PADOG_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PADOG_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PADOG_1.38.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA dependencyCount: 61 Package: pageRank Version: 1.6.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 111d7d672c222f8c881f5509e0f0b0ea NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: RELEASE_3_15 git_last_commit: 5484300 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pageRank_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pageRank_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pageRank_1.6.0.tgz vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 126 Package: PAIRADISE Version: 1.12.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 0946884bd10402c2dbd4c5956ac88467 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: RELEASE_3_15 git_last_commit: 8307ade git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PAIRADISE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAIRADISE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAIRADISE_1.12.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 67 Package: paircompviz Version: 1.34.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 1c83b2c738bcb9e680204ac69f7a0920 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: RELEASE_3_15 git_last_commit: c35c44c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/paircompviz_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/paircompviz_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/paircompviz_1.34.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 10 Package: pairkat Version: 1.2.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 MD5sum: e753d7746a2ca5f3a9c065890ba1b730 NeedsCompilation: no Title: PaIRKAT Description: PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. biocViews: Software, Metabolomics, KEGG, Pathways, Network, GraphAndNetwork, Regression Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut] Maintainer: Max McGrath VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: RELEASE_3_15 git_last_commit: c5d1a96 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pairkat_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pairkat_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pairkat_1.2.0.tgz vignettes: vignettes/pairkat/inst/doc/using-pairkat.html vignetteTitles: using-pairkat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R dependencyCount: 51 Package: pandaR Version: 1.28.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 4062c636a3e5bcc9feb649c5d7e4f825 NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: RELEASE_3_15 git_last_commit: 49eaff8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pandaR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pandaR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pandaR_1.28.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependsOnMe: netZooR dependencyCount: 45 Package: panelcn.mops Version: 1.18.0 Depends: R (>= 3.5.0), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: ff338115510090fa5b30187e6d804d78 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre] Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_15 git_last_commit: 1312e4a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/panelcn.mops_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/panelcn.mops_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/panelcn.mops_1.18.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 33 Package: PanomiR Version: 1.0.2 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma, metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer, rlang, tibble, withr, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 8193909778553ff820dfab1a6e2e3f3f NeedsCompilation: no Title: Detection of miRNAs that regulate interacting groups of pathways Description: PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data. biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA, Pathways Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut] Maintainer: Pourya Naderi URL: https://github.com/pouryany/PanomiR VignetteBuilder: knitr BugReports: https://github.com/pouryany/PanomiR/issues git_url: https://git.bioconductor.org/packages/PanomiR git_branch: RELEASE_3_15 git_last_commit: f1c15ea git_last_commit_date: 2022-08-17 Date/Publication: 2022-08-18 source.ver: src/contrib/PanomiR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/PanomiR_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/PanomiR_1.0.2.tgz vignettes: vignettes/PanomiR/inst/doc/PanomiR.html vignetteTitles: PanomiR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R dependencyCount: 159 Package: panp Version: 1.66.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: b83411b7daf705dd2e55d68b9635c8f5 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: RELEASE_3_15 git_last_commit: 7cc7806 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/panp_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/panp_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/panp_1.66.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 12 Package: PANR Version: 1.42.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 30f369fa996601ae103a7eed84f8c3d5 NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: RELEASE_3_15 git_last_commit: e22d4d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PANR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PANR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PANR_1.42.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 15 Package: pareg Version: 1.0.0 Depends: R (>= 4.2), tensorflow (>= 2.2.0), tfprobability (>= 0.10.0) Imports: stats, tidyr, purrr, furrr, tibble, glue, tidygraph, igraph, proxy, dplyr, magrittr, ggplot2, ggraph, rlang, progress, Matrix, matrixLaplacian, keras, nloptr, shadowtext, methods, DOSE, stringr, reticulate Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, devtools, plotROC, PRROC, mgsa, topGO, msigdbr, betareg, fgsea, ComplexHeatmap, GGally, ggsignif, circlize, enrichplot, ggnewscale, tidyverse, cowplot, ggfittext License: GPL-3 MD5sum: a61a4f439696635d0a5adff2b3bcd7d7 NeedsCompilation: no Title: Pathway enrichment using a regularized regression approach Description: Compute pathway enrichment scores while accounting for term-term relations. This package uses a regularized multiple linear regression to regress differential expression p-values obtained from multi-condition experiments on a pathway membership matrix. By doing so, it is able to incorporate additional biological knowledge into the enrichment analysis and to estimate pathway enrichment scores more robustly. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network Author: Kim Philipp Jablonski [aut, cre] () Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/pareg VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/pareg/issues git_url: https://git.bioconductor.org/packages/pareg git_branch: RELEASE_3_15 git_last_commit: 6a94a38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pareg_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pareg_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pareg_1.0.0.tgz vignettes: vignettes/pareg/inst/doc/pareg.html, vignettes/pareg/inst/doc/pathway_similarities.html vignetteTitles: Get started, Pathway similarities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pareg/inst/doc/pareg.R, vignettes/pareg/inst/doc/pathway_similarities.R dependencyCount: 154 Package: parglms Version: 1.28.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: aa250c75bf51cf9deb870b769c7dffba NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: RELEASE_3_15 git_last_commit: e52bec5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/parglms_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/parglms_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/parglms_1.28.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 36 Package: parody Version: 1.54.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: d1b04f41bdbdfb464a86b9ba35456efb NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: Vince Carey [aut, cre] () Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: RELEASE_3_15 git_last_commit: c645cbf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/parody_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/parody_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/parody_1.54.0.tgz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: PAST Version: 1.12.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 9686d7c32c7af88857f1fd75fed15d73 NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: RELEASE_3_15 git_last_commit: 4659dad git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PAST_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAST_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAST_1.12.0.tgz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 85 Package: Path2PPI Version: 1.26.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 07d75ad19baaffe9cc1e738ef44777be NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: RELEASE_3_15 git_last_commit: c1ba384 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Path2PPI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Path2PPI_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Path2PPI_1.26.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 12 Package: pathifier Version: 1.34.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 301d4c7100cc490b8fa06f317c5f1366 NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_15 git_last_commit: 2500ff2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pathifier_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathifier_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathifier_1.34.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R dependencyCount: 9 Package: PathNet Version: 1.36.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 8bfd1460f218827b13e5b89ef3b9061f NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: RELEASE_3_15 git_last_commit: 98c3948 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PathNet_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PathNet_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PathNet_1.36.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: PathoStat Version: 1.22.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: b9af30d017d859411e3dde5581823f0f NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: RELEASE_3_15 git_last_commit: e6a7bac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PathoStat_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PathoStat_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PathoStat_1.22.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 214 Package: pathRender Version: 1.64.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 04abd62b7fabd6199d997f5f9b197578 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: RELEASE_3_15 git_last_commit: 2405012 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pathRender_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathRender_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathRender_1.64.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 50 Package: pathVar Version: 1.26.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 01316bd3c3d36979bb11880c52136140 NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/pathVar git_branch: RELEASE_3_15 git_last_commit: f5bbc08 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pathVar_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathVar_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathVar_1.26.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R dependencyCount: 41 Package: pathview Version: 1.36.1 Depends: R (>= 3.5.0) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 8d3d14863d6f341442f9ff5b7c565ecc NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: RELEASE_3_15 git_last_commit: f2e86b1 git_last_commit_date: 2022-08-27 Date/Publication: 2022-08-28 source.ver: src/contrib/pathview_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathview_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.2/pathview_1.36.1.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: BioNetStat, EGSEA, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, MAGeCKFlute, TCGAbiolinksGUI, TCGAWorkflow, lilikoi suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow dependencyCount: 51 Package: pathwayPCA Version: 1.12.0 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: 86cdaf9436170c942f12cee0cebcb0c4 NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: RELEASE_3_15 git_last_commit: e3fc7f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pathwayPCA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathwayPCA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathwayPCA_1.12.0.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R importsMe: fcoex dependencyCount: 12 Package: paxtoolsr Version: 1.30.0 Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr, rappdirs Suggests: testthat, knitr, BiocStyle, formatR, rmarkdown, RColorBrewer, foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler License: LGPL-3 MD5sum: 1d100b692ed3a9697d15b32c32a0c7df NeedsCompilation: no Title: Access Pathways from Multiple Databases Through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database. Pathway Commons is a project by the Memorial Sloan-Kettering Cancer Center (MSKCC), Dana-Farber Cancer Institute (DFCI), and the University of Toronto. Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna [aut, cre] Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/paxtoolsr git_branch: RELEASE_3_15 git_last_commit: 191ee3c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/paxtoolsr_1.30.0.tar.gz vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R suggestsMe: netboxr dependencyCount: 52 Package: pcaExplorer Version: 2.22.0 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: ef8299c9cdebd618d2fa4da48e17078f NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: RELEASE_3_15 git_last_commit: ce2a13b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pcaExplorer_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcaExplorer_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcaExplorer_2.22.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R importsMe: ideal dependencyCount: 182 Package: pcaMethods Version: 1.88.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 14d5e38c982302850c1093d2006f0da4 NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: RELEASE_3_15 git_last_commit: 02fb58d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pcaMethods_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcaMethods_1.88.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcaMethods_1.88.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD importsMe: autonomics, consensusDE, destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde, SomaticSignatures, ADAPTS, CopSens, geneticae, LOST, MetabolomicsBasics, missCompare, multiDimBio, polyRAD, promor, RAMClustR, santaR, scMappR suggestsMe: MsCoreUtils, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 9 Package: PCAN Version: 1.24.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 52199a0b43a5749317c72fccbb448fc2 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: RELEASE_3_15 git_last_commit: 93eeab6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PCAN_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PCAN_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PCAN_1.24.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 13 Package: PCAtools Version: 2.8.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 69e5f0e3deee93c123cce8929a50cbb3 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: RELEASE_3_15 git_last_commit: a285fdc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PCAtools_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PCAtools_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PCAtools_2.8.0.tgz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R dependsOnMe: OSCA.advanced suggestsMe: scDataviz dependencyCount: 69 Package: pcxn Version: 2.18.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE MD5sum: c2dddfd37ffab896b619838ea36290fe NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pcxn git_branch: RELEASE_3_15 git_last_commit: f167dc3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pcxn_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcxn_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcxn_2.18.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R suggestsMe: pcxnData dependencyCount: 21 Package: PDATK Version: 1.4.0 Depends: R (>= 4.1), SummarizedExperiment Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus, igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro, CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics, survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel, rlang, piano, scales, survcomp, genefu, ggplot2, switchBox, reportROC, pROC, verification, utils Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array License: MIT + file LICENSE Archs: x64 MD5sum: 5368bdb5c402de7f436807cc481226d4 NeedsCompilation: no Title: Pancreatic Ductal Adenocarcinoma Tool-Kit Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification, Survival, Clustering, GenePrediction Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles [aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: RELEASE_3_15 git_last_commit: e8151e0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PDATK_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PDATK_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PDATK_1.4.0.tgz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor, PDATK_introduction.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R, vignettes/PDATK/inst/doc/PDATK_introduction.R dependencyCount: 263 Package: pdInfoBuilder Version: 1.60.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: e10c3b196e08d59594f1ee0754b789da NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: RELEASE_3_15 git_last_commit: d8b7f18 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pdInfoBuilder_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pdInfoBuilder_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pdInfoBuilder_1.60.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 53 Package: PeacoQC Version: 1.6.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) MD5sum: 9e8346036cf9e76c43dd52f8123f836f NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: RELEASE_3_15 git_last_commit: 2fa1046 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PeacoQC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PeacoQC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PeacoQC_1.6.0.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.html vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R dependencyCount: 97 Package: peakPantheR Version: 1.10.0 Depends: R (>= 4.2) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.3.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 Archs: x64 MD5sum: c4606face54ee36d854df47ceac25e49 NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (), Goncalo Correia [aut] (), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: RELEASE_3_15 git_last_commit: 28d6792 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/peakPantheR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/peakPantheR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/peakPantheR_1.10.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 109 Package: PECA Version: 1.32.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 8d6157e1a28be84a7478d25ba22ad2fb NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: RELEASE_3_15 git_last_commit: df1b5c7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PECA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PECA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PECA_1.32.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 85 Package: peco Version: 1.8.0 Depends: R (>= 3.5.0) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: 5977535e64373b2d63e32599ddcc702e NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: RELEASE_3_15 git_last_commit: 4f0d7d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/peco_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/peco_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/peco_1.8.0.tgz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 96 Package: pengls Version: 1.2.0 Depends: R (>= 4.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: 21c561f17702f532551d00043f98ac37 NeedsCompilation: no Title: Fit Penalised Generalised Least Squares models Description: Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data. biocViews: Transcriptomics, Regression, TimeCourse Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls git_url: https://git.bioconductor.org/packages/pengls git_branch: RELEASE_3_15 git_last_commit: 0cbde07 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pengls_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pengls_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pengls_1.2.0.tgz vignettes: vignettes/pengls/inst/doc/penglsVignette.html vignetteTitles: Vignette of the pengls package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pengls/inst/doc/penglsVignette.R dependencyCount: 26 Package: PepsNMR Version: 1.14.0 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: a161d96d4315dc3920dc344df67724ce NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: RELEASE_3_15 git_last_commit: cdeb17c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PepsNMR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PepsNMR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PepsNMR_1.14.0.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 47 Package: pepStat Version: 1.30.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 84eace2ee6d39121b8a44ccfefc425b4 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: RELEASE_3_15 git_last_commit: 4478125 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pepStat_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pepStat_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pepStat_1.30.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 59 Package: pepXMLTab Version: 1.30.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d208f4809e853c43f472a315ffd376c9 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: RELEASE_3_15 git_last_commit: 94d1282 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pepXMLTab_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pepXMLTab_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pepXMLTab_1.30.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: PERFect Version: 1.10.0 Depends: R (>= 3.6.0), sn (>= 1.5.2) Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3), psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14), fitdistrplus (>= 1.0.12), parallel (>= 3.6.0) Suggests: knitr, rmarkdown, kableExtra, ggpubr License: Artistic-2.0 MD5sum: 9b3786fbc6ee33a7a9585e5ee88197b1 NeedsCompilation: no Title: Permutation filtration for microbiome data Description: PERFect is a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds, and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. biocViews: Software, Microbiome, Sequencing, Classification, Metagenomics Author: Ekaterina Smirnova , Quy Cao Maintainer: Quy Cao URL: https://github.com/cxquy91/PERFect VignetteBuilder: knitr BugReports: https://github.com/cxquy91/PERFect/issues git_url: https://git.bioconductor.org/packages/PERFect git_branch: RELEASE_3_15 git_last_commit: e0a5ccf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PERFect_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PERFect_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PERFect_1.10.0.tgz vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html vignetteTitles: Method Illustration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R dependencyCount: 85 Package: periodicDNA Version: 1.6.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE MD5sum: c24f71156b779a296c3bfa2ae3ddc42b NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: RELEASE_3_15 git_last_commit: dd2df94 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/periodicDNA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/periodicDNA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/periodicDNA_1.6.0.tgz vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 76 Package: PFP Version: 1.4.0 Depends: R (>= 4.0) Imports: graph, igraph, KEGGgraph, clusterProfiler, ggplot2, plyr, tidyr, magrittr, stats, methods, utils Suggests: knitr, testthat, rmarkdown, org.Hs.eg.db License: GPL-2 MD5sum: d2ab9f3194430604e332fe5f34da5c62 NeedsCompilation: no Title: Pathway Fingerprint Framework in R Description: An implementation of the pathway fingerprint framework that introduced in paper "Pathway Fingerprint: a novel pathway knowledge and topology based method for biomarker discovery and characterization". This method provides a systematic comparisons between a gene set (such as a list of differentially expressed genes) and well-studied "basic pathway networks" (KEGG pathways), measuring the importance of pathways and genes for the gene set. The package is helpful for researchers to find the biomarkers and its function. biocViews: Software, Pathways, RNASeq Author: XC Zhang [aut, cre] Maintainer: XC Zhang URL: https://github.com/aib-group/PFP VignetteBuilder: knitr BugReports: https://github.com/aib-group/PFP/issues git_url: https://git.bioconductor.org/packages/PFP git_branch: RELEASE_3_15 git_last_commit: 9c8376b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PFP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PFP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PFP_1.4.0.tgz vignettes: vignettes/PFP/inst/doc/PFP.html vignetteTitles: Pathway fingerprint: a tool for biomarker discovery based on gene expression data and pathway knowledge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PFP/inst/doc/PFP.R dependencyCount: 131 Package: pgca Version: 1.20.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 2017047e0f262fa11711e07a3e2a24d3 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: RELEASE_3_15 git_last_commit: c937244 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pgca_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pgca_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pgca_1.20.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: phantasus Version: 1.16.2 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, data.table, curl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: cf81b500bb2489486e826410a828f76d NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina [ctb], Maxim Kleverov [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_15 git_last_commit: 2bc288f git_last_commit_date: 2022-05-05 Date/Publication: 2022-05-15 source.ver: src/contrib/phantasus_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/phantasus_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.2/phantasus_1.16.2.tgz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 149 Package: PharmacoGx Version: 3.0.2 Depends: R (>= 3.6), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, checkmate, boot, coop LinkingTo: Rcpp Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown License: Artistic-2.0 MD5sum: a8c9e953f324696fc605ba2cce6c2b19 NeedsCompilation: yes Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Zhaleh Safikhani [aut], Christopher Eeles [aut], Mark Freeman [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_15 git_last_commit: 58de32f git_last_commit_date: 2022-06-03 Date/Publication: 2022-06-05 source.ver: src/contrib/PharmacoGx_3.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/PharmacoGx_3.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/PharmacoGx_3.0.2.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf, vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf vignetteTitles: Creating a PharmacoSet Object, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: Xeva suggestsMe: ToxicoGx dependencyCount: 140 Package: phemd Version: 1.12.0 Depends: R (>= 3.5), monocle, Seurat Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase, phateR, reticulate Suggests: knitr License: GPL-2 MD5sum: 17a39657b97580dd22f524f6fdca03f5 NeedsCompilation: no Title: Phenotypic EMD for comparison of single-cell samples Description: Package for comparing and generating a low-dimensional embedding of multiple single-cell samples. biocViews: Clustering, ComparativeGenomics, Proteomics, Transcriptomics, Sequencing, DimensionReduction, SingleCell, DataRepresentation, Visualization, MultipleComparison Author: William S Chen [aut, cre] Maintainer: William S Chen VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/phemd git_branch: RELEASE_3_15 git_last_commit: 2802049 git_last_commit_date: 2022-09-06 Date/Publication: 2022-09-08 source.ver: src/contrib/phemd_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phemd_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phemd_1.12.0.tgz vignettes: vignettes/phemd/inst/doc/phemd.html vignetteTitles: PhEMD vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phemd/inst/doc/phemd.R dependencyCount: 237 Package: PhenoGeneRanker Version: 1.4.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License Archs: x64 MD5sum: 81636f9dd971ba3424332f608b988172 NeedsCompilation: no Title: PhenoGeneRanker: A gene and phenotype prioritization tool Description: This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155. biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork, Network, NetworkInference, Pathways, Software, SystemsBiology Author: Cagatay Dursun [aut, cre] Maintainer: Cagatay Dursun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: RELEASE_3_15 git_last_commit: 2f702cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PhenoGeneRanker_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhenoGeneRanker_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhenoGeneRanker_1.4.0.tgz vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html vignetteTitles: PhenoGeneRanker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R dependencyCount: 30 Package: phenopath Version: 1.20.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: f0ee81acfb9914a09edb3c1db72f7574 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: RELEASE_3_15 git_last_commit: 7642e47 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/phenopath_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phenopath_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phenopath_1.20.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 61 Package: phenoTest Version: 1.44.0 Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: 8d0ece1fdb58603dad26b1d681b16905 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: RELEASE_3_15 git_last_commit: dab00a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/phenoTest_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phenoTest_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phenoTest_1.44.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 141 Package: PhenStat Version: 2.32.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE Archs: x64 MD5sum: 279331b79e82e7ad39b791dc3ac69e0b NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: RELEASE_3_15 git_last_commit: 2e144f6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PhenStat_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhenStat_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhenStat_2.32.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf vignetteTitles: PhenStat Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R dependencyCount: 106 Package: philr Version: 1.22.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 9a234ef09e2e89392e67598df22ebf62 NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman [aut, cre], Leo Lahti [ctb] () Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: RELEASE_3_15 git_last_commit: 4fb75bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/philr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/philr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/philr_1.22.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R dependencyCount: 62 Package: PhIPData Version: 1.4.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE MD5sum: c8c4916820e7cee6b4af93a1011a46e5 NeedsCompilation: no Title: Container for PhIP-Seq Experiments Description: PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage Author: Athena Chen [aut, cre] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: RELEASE_3_15 git_last_commit: 1da367a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PhIPData_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhIPData_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhIPData_1.4.0.tgz vignettes: vignettes/PhIPData/inst/doc/PhIPData.html vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R dependsOnMe: beer dependencyCount: 67 Package: phosphonormalizer Version: 1.20.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 372dde26f2486ed938d71d219814adfc NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: RELEASE_3_15 git_last_commit: bb4bfdf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/phosphonormalizer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phosphonormalizer_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phosphonormalizer_1.20.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.6.0 Depends: R (>= 4.1.0) Imports: ruv, e1071, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils, SummarizedExperiment, methods, S4Vectors, BiocGenerics, ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown, org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle, stringr, calibrate License: GPL-3 + file LICENSE MD5sum: 8bfc58d366e46022f9138502d4c2180e NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, Proteomics Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Hani Jieun Kim [aut] Maintainer: Taiyun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: RELEASE_3_15 git_last_commit: 763396b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PhosR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhosR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhosR_1.6.0.tgz vignettes: vignettes/PhosR/inst/doc/PhosR.html vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R dependencyCount: 148 Package: PhyloProfile Version: 1.10.5 Depends: R (>= 4.2.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, RCurl, shiny, shinyBS, shinyFiles, shinyjs, OmaDB, plyr, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e84c19c0ac3e82b47dc780ea8b96ea19 NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction Author: Vinh Tran [aut, cre], Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: RELEASE_3_15 git_last_commit: b8902d5 git_last_commit_date: 2022-08-02 Date/Publication: 2022-08-02 source.ver: src/contrib/PhyloProfile_1.10.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhyloProfile_1.10.5.zip mac.binary.ver: bin/macosx/contrib/4.2/PhyloProfile_1.10.5.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 139 Package: phyloseq Version: 1.40.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: 3545b6fcf82d0a56f28d550880a4d3c2 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_15 git_last_commit: 20bb27d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/phyloseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phyloseq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phyloseq_1.40.0.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT, phyloseqGraphTest importsMe: benchdamic, combi, MBECS, metavizr, microbiomeDASim, microbiomeMarker, PathoStat, PERFect, RCM, reconsi, RPA, SPsimSeq, HMP2Data, adaptiveGPCA, corncob, HTSSIP, microbial, MicrobiomeStat, mixKernel, SigTree, treeDA suggestsMe: CBEA, decontam, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, fido, file2meco, metacoder, PLNmodels dependencyCount: 75 Package: Pi Version: 2.8.0 Depends: igraph, dnet, ggplot2, graphics Imports: Matrix, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, lattice, caret, plot3D, stats, methods, MASS, IRanges, BiocGenerics, dplyr, tidyr, ggnetwork, osfr, RCircos, purrr, readr, tibble Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ggforce, fgsea, RColorBrewer, ggpubr, rtracklayer, ggbio, Gviz, data.tree, jsonlite License: GPL-3 MD5sum: 1847ebfe7f7026af6ec87de5fee39294 NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system. It integrates functional genomic predictors, knowledge of network connectivity and immune ontologies to prioritise potential drug targets at the gene and pathway level. biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues git_url: https://git.bioconductor.org/packages/Pi git_branch: RELEASE_3_15 git_last_commit: 019a211 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Pi_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pi_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pi_2.8.0.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R dependencyCount: 142 Package: piano Version: 2.12.1 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: 31736951dc81c8542b47ac399870391d NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_15 git_last_commit: a9347d9 git_last_commit_date: 2022-09-13 Date/Publication: 2022-09-13 source.ver: src/contrib/piano_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/piano_2.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/piano_2.12.1.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx, PDATK suggestsMe: cosmosR, BloodCancerMultiOmics2017 dependencyCount: 95 Package: pickgene Version: 1.68.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 46ced917233fe5ba7af4ba25f7e9efd5 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: RELEASE_3_15 git_last_commit: 0c68dd6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pickgene_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pickgene_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pickgene_1.68.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: PICS Version: 2.40.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 MD5sum: 65e74b98f78f34b1c8605dacc0de879f NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPseq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud URL: https://github.com/SRenan/PICS VignetteBuilder: knitr BugReports: https://github.com/SRenan/PICS/issues git_url: https://git.bioconductor.org/packages/PICS git_branch: RELEASE_3_15 git_last_commit: dce082e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PICS_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PICS_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PICS_2.40.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.html vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING dependencyCount: 39 Package: Pigengene Version: 1.22.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.18.1) Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI, DOSE Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy License: GPL (>=2) MD5sum: 58765bc2b728a1ec6fc796bd7cc5348f NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short, Isha Mehta, and Neda Emami Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_15 git_last_commit: 9b1948a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Pigengene_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pigengene_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pigengene_1.22.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R dependencyCount: 186 Package: PING Version: 2.40.0 Depends: R(>= 3.5.0) Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: x64 MD5sum: 9d34079cf868dd5a25b725ecbd8e956a NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: RELEASE_3_15 git_last_commit: 29b5fa4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PING_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PING_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PING_2.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 166 Package: pipeComp Version: 1.6.0 Depends: R (>= 4.1) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle, rmarkdown License: GPL MD5sum: 9e66d83c7216bf848cef223d22af5d4f NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (), Anthony Sonrel [aut] (), Mark D. Robinson [aut, fnd] () Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: RELEASE_3_15 git_last_commit: 1ed6a6d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pipeComp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pipeComp_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pipeComp_1.6.0.tgz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 208 Package: pipeFrame Version: 1.12.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 Archs: x64 MD5sum: 6f037310150f00f47cde1efc2d2b88fb NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: RELEASE_3_15 git_last_commit: 0e7f3c7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pipeFrame_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pipeFrame_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pipeFrame_1.12.0.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: enrichTF, esATAC dependencyCount: 72 Package: pkgDepTools Version: 1.62.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 MD5sum: 64feb2980fbcaea8c305f41d6576f7af NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/pkgDepTools git_branch: RELEASE_3_15 git_last_commit: c448b71 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pkgDepTools_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pkgDepTools_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pkgDepTools_1.62.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R dependencyCount: 9 Package: planet Version: 1.4.0 Depends: R (>= 4.0) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: e6b0301fb24f826ac3db3720ebda35ec NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to infer additional biological variables to supplemental DNA methylation analysis of placental data. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. The package comes with an example processed placental dataset. biocViews: Software, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, DNAMethylation, CpGIsland Author: Victor Yuan [aut, cre], Wendy P. Robinson [ctb] Maintainer: Victor Yuan URL: https://victor.rbind.io/planet, http://github.com/wvictor14/planet VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: RELEASE_3_15 git_last_commit: afd5594 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/planet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/planet_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/planet_1.4.0.tgz vignettes: vignettes/planet/inst/doc/planet.html vignetteTitles: planet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planet/inst/doc/planet.R importsMe: methylclock dependencyCount: 21 Package: plethy Version: 1.34.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 69ca81495d9fbacecb0d4c3a74b2332d NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly git_url: https://git.bioconductor.org/packages/plethy git_branch: RELEASE_3_15 git_last_commit: 6c951e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/plethy_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plethy_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plethy_1.34.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R dependencyCount: 61 Package: plgem Version: 1.68.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: 75e8fa025a0cb3da3e7619b5a159e8a5 NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: RELEASE_3_15 git_last_commit: cbd567e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/plgem_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plgem_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plgem_1.68.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 8 Package: plier Version: 1.66.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: a04bb99cd2b4cca3bcd8e2dd45abff43 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: RELEASE_3_15 git_last_commit: 116f796 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/plier_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plier_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plier_1.66.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: PloGO2 Version: 1.8.2 Depends: R (>= 4.0), GO.db, GOstats Imports: lattice, httr, openxlsx, xtable License: GPL-2 MD5sum: 9e02973e152484a0875c4325cb969b41 NeedsCompilation: no Title: Plot Gene Ontology and KEGG pathway Annotation and Abundance Description: Functions for enrichment analysis and plotting gene ontology or KEGG pathway information for multiple data subsets at the same time. It also enables encorporating multiple conditions and abundance data. biocViews: Annotation, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison, Pathways, Software, Visualization Author: Dana Pascovici, Jemma Wu Maintainer: Jemma Wu , Dana Pascovici git_url: https://git.bioconductor.org/packages/PloGO2 git_branch: RELEASE_3_15 git_last_commit: f2c093f git_last_commit_date: 2022-09-15 Date/Publication: 2022-09-15 source.ver: src/contrib/PloGO2_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/PloGO2_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/PloGO2_1.8.2.tgz vignettes: vignettes/PloGO2/inst/doc/PloGO2_vignette.pdf, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PloGO2/inst/doc/PloGO2_vignette.R, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.R dependencyCount: 66 Package: plotgardener Version: 1.2.10 Depends: R (>= 4.1.0) Imports: curl, data.table, dplyr, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rlang, stats, strawr, tools, utils LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, GenomeInfoDb, GenomicRanges, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene License: MIT + file LICENSE Archs: x64 MD5sum: 85ae0c30f6646f6a6ecc7fa6ea3b4d37 NeedsCompilation: yes Title: Coordinate-Based Genomic Visualization Package for R Description: Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. biocViews: Visualization, GenomeAnnotation, FunctionalGenomics, GenomeAssembly, HiC Author: Nicole Kramer [aut, cre] (), Eric S. Davis [aut] (), Craig Wenger [aut] (), Sarah Parker [ctb], Erika Deoudes [art], Michael Love [ctb], Douglas H. Phanstiel [aut, cph] Maintainer: Nicole Kramer URL: https://phanstiellab.github.io/plotgardener, https://github.com/PhanstielLab/plotgardener VignetteBuilder: knitr BugReports: https://github.com/PhanstielLab/plotgardener/issues git_url: https://git.bioconductor.org/packages/plotgardener git_branch: RELEASE_3_15 git_last_commit: 83f73b5 git_last_commit_date: 2022-08-04 Date/Publication: 2022-08-07 source.ver: src/contrib/plotgardener_1.2.10.tar.gz win.binary.ver: bin/windows/contrib/4.2/plotgardener_1.2.10.zip mac.binary.ver: bin/macosx/contrib/4.2/plotgardener_1.2.10.tgz vignettes: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html vignetteTitles: Introduction to plotgardener hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R importsMe: Ularcirc suggestsMe: nullranges dependencyCount: 85 Package: plotGrouper Version: 1.14.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: 48c1f1204062d21ff502a39685c6b43b NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: RELEASE_3_15 git_last_commit: e47570c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/plotGrouper_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plotGrouper_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plotGrouper_1.14.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 147 Package: PLPE Version: 1.56.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) Archs: x64 MD5sum: 27d04f1d97ee292644e6a47d458bfad5 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: RELEASE_3_15 git_last_commit: d12dc5a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PLPE_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PLPE_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PLPE_1.56.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 9 Package: plyranges Version: 1.16.0 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: 707c076f30a3e04b4eae5b199a5142b6 NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut, cre] (), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] () Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://github.com/sa-lee/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_15 git_last_commit: 6d76588 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/plyranges_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plyranges_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plyranges_1.16.0.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BOBaFIT, BUSpaRse, dasper, EpiCompare, InPAS, methylCC, multicrispr, nearBynding, nullranges, plotgardener, fluentGenomics suggestsMe: extraChIPs, memes, svaNUMT, svaRetro dependencyCount: 61 Package: pmm Version: 1.28.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: c93586c6fe6b017165137f57eed52dec NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: RELEASE_3_15 git_last_commit: 4bb6378 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pmm_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pmm_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pmm_1.28.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 50 Package: pmp Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: c377ad507948b56fdeb3bdee07ae0ab3 NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: RELEASE_3_15 git_last_commit: 3c77e26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pmp_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pmp_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pmp_1.8.0.tgz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R suggestsMe: metabolomicsWorkbenchR, structToolbox dependencyCount: 69 Package: PoDCall Version: 1.4.0 Depends: R (>= 4.2) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: e8fb7f4fcf2449c4b03010f03ec9e574 NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from 'QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall. biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation, CpGIsland, DNAMethylation, Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: RELEASE_3_15 git_last_commit: a98cc0f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PoDCall_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PoDCall_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PoDCall_1.4.0.tgz vignettes: vignettes/PoDCall/inst/doc/PoDCall.html vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation ddPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R dependencyCount: 86 Package: podkat Version: 1.28.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) MD5sum: f9d4d7a9b7b72a532b99a337bd66c824 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_15 git_last_commit: 0704aa4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/podkat_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/podkat_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/podkat_1.28.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 47 Package: pogos Version: 1.16.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1bc4fca1b40d2bb6a32ed3f602faefdb NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: RELEASE_3_15 git_last_commit: 8e5877e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pogos_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pogos_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pogos_1.16.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK dependencyCount: 111 Package: polyester Version: 1.32.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown, markdown License: Artistic-2.0 Archs: x64 MD5sum: 39333815db598e1be8b54921f1efc563 NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/polyester git_branch: RELEASE_3_15 git_last_commit: dfdd878 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/polyester_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/polyester_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/polyester_1.32.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R dependencyCount: 20 Package: POMA Version: 1.6.0 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dplyr, e1071, ggplot2, ggrepel, glasso (>= 1.11), glmnet, impute, knitr, limma, magrittr, mixOmics, randomForest, RankProd (>= 3.14), rmarkdown, SummarizedExperiment, tibble, tidyr, vegan Suggests: BiocStyle, covr, ggraph, patchwork, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 51c225611f34b9134d31e44b6ab7e8d7 NeedsCompilation: no Title: User-friendly Workflow for Omics Data Analysis Description: A structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploration, and statistical analysis of omics datasets. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package also has a Shiny app version that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. biocViews: MassSpectrometry, Metabolomics, Proteomics, Software, StatisticalMethod, Visualization, Preprocessing, Normalization, ReportWriting Author: Pol Castellano-Escuder [aut, cre] () Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: RELEASE_3_15 git_last_commit: 2ed1380 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/POMA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/POMA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/POMA_1.6.0.tgz vignettes: vignettes/POMA/inst/doc/POMA-demo.html, vignettes/POMA/inst/doc/POMA-eda.html, vignettes/POMA/inst/doc/POMA-normalization.html vignetteTitles: POMA Workflow, POMA EDA Example, POMA Normalization Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-demo.R, vignettes/POMA/inst/doc/POMA-eda.R, vignettes/POMA/inst/doc/POMA-normalization.R suggestsMe: fobitools dependencyCount: 153 Package: PoTRA Version: 1.12.0 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL MD5sum: 9bd552e00ab33b46e59d44c9a7462f5a NeedsCompilation: no Title: PoTRA: Pathways of Topological Rank Analysis Description: The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease (Li, Liu & Dinu, 2018). We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fishers Exact test to determine if the number of hub genes in each pathway is altered from normal to cancer (Li, Liu & Dinu, 2018). Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer (Li, Liu & Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes (Li, Liu & Dinu, 2018). PoTRA can be used with the KEGG, Reactome, SMPDB and PharmGKB, Panther, PathBank, etc databases from the devel graphite library. biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression, DifferentialExpression, Pathways, Reactome, Network, KEGG, PathBank, Panther Author: Chaoxing Li, Li Liu and Valentin Dinu Maintainer: Margaret Linan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoTRA git_branch: RELEASE_3_15 git_last_commit: 23ce63f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PoTRA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PoTRA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PoTRA_1.12.0.tgz vignettes: vignettes/PoTRA/inst/doc/PoTRA.html vignetteTitles: Pathways of Topological Rank Analysis (PoTRA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R dependencyCount: 54 Package: powerTCR Version: 1.16.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: bc87602b411688d095906a14357f67c5 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: RELEASE_3_15 git_last_commit: b34a180 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/powerTCR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/powerTCR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/powerTCR_1.16.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R importsMe: scRepertoire dependencyCount: 32 Package: POWSC Version: 1.4.0 Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST Imports: pheatmap, ggplot2, RColorBrewer, grDevices, SummarizedExperiment, limma Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: c6d2812a8fb26036da4649d0d2ecf0ff NeedsCompilation: no Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: RELEASE_3_15 git_last_commit: df13516 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/POWSC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/POWSC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/POWSC_1.4.0.tgz vignettes: vignettes/POWSC/inst/doc/POWSC.html vignetteTitles: The POWSC User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POWSC/inst/doc/POWSC.R dependencyCount: 70 Package: ppcseq Version: 1.4.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), tibble, dplyr, magrittr, purrr, future, furrr, tidyr (>= 0.8.3.9000), lifecycle, ggplot2, foreach, tidybayes, edgeR, benchmarkme, parallel, rlang, stats, utils, graphics LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 MD5sum: a75325cb2ccbd64837bc53f42d182182 NeedsCompilation: yes Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models Description: Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers. biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] () Maintainer: Stefano Mangiola SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: RELEASE_3_15 git_last_commit: c4336d1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ppcseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ppcseq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ppcseq_1.4.0.tgz vignettes: vignettes/ppcseq/inst/doc/introduction.html vignetteTitles: Overview of the ppcseq package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppcseq/inst/doc/introduction.R dependencyCount: 102 Package: PPInfer Version: 1.22.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: fb82f38e912adc6e9de78135371c5a7d NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: RELEASE_3_15 git_last_commit: 6473805 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PPInfer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PPInfer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PPInfer_1.22.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 117 Package: ppiStats Version: 1.62.0 Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable, ppiData, ScISI License: Artistic-2.0 MD5sum: c5dcae4e1db996b5c63edeb62d071931 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/ppiStats git_branch: RELEASE_3_15 git_last_commit: 756eb84 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ppiStats_1.62.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ppiStats_1.62.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R suggestsMe: RpsiXML, ppiData dependencyCount: 60 Package: pqsfinder Version: 2.12.0 Depends: R (>= 3.5.0), Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.78.0) Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: ba26b068ec8ce57e6d13a14e61ce5668 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: RELEASE_3_15 git_last_commit: 34cb8e0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pqsfinder_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pqsfinder_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pqsfinder_2.12.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 21 Package: pram Version: 1.12.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0), GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: 0cb19d2c3fb4042bbe45e150b67950fa NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq, BiologicalQuestion, GenePrediction, GenomeAnnotation, ResearchField, Transcriptomics Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: RELEASE_3_15 git_last_commit: 181f95b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pram_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pram_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pram_1.12.0.tgz vignettes: vignettes/pram/inst/doc/pram.pdf vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 46 Package: prebs Version: 1.36.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 Archs: x64 MD5sum: f54c3ff51890a89c27cff5f84a7079de NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: RELEASE_3_15 git_last_commit: cf1fa9d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/prebs_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/prebs_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/prebs_1.36.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 115 Package: preciseTAD Version: 1.6.0 Depends: R (>= 4.1) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, gtools, rCGH Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: 2af8a3001c1cca444c4c1d444fde8721 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: RELEASE_3_15 git_last_commit: 4b322f8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/preciseTAD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/preciseTAD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/preciseTAD_1.6.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 179 Package: PrecisionTrialDrawer Version: 1.11.0 Depends: R (>= 3.6) Imports: graphics, grDevices, stats, utils, methods, cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, BiocParallel, magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid, S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation, googleVis, shiny, shinyBS, DT, brglm, matrixStats Suggests: BiocStyle, knitr, rmarkdown, dplyr License: GPL-3 MD5sum: 5c58ef971e5d38be33ed5a1ff065982e NeedsCompilation: no Title: A Tool to Analyze and Design NGS Based Custom Gene Panels Description: A suite of methods to design umbrella and basket trials for precision oncology. biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport, GeneExpression Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella Maintainer: Giorgio Melloni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PrecisionTrialDrawer git_branch: master git_last_commit: 52b19d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-28 source.ver: src/contrib/PrecisionTrialDrawer_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PrecisionTrialDrawer_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PrecisionTrialDrawer_1.11.0.tgz vignettes: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.html vignetteTitles: Bioconductor style for HTML documents hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.R dependencyCount: 128 Package: PREDA Version: 1.42.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 27f5e5fd5f0567fbda99dc6167497617 NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_15 git_last_commit: ac1b96e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PREDA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PREDA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PREDA_1.42.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 57 Package: preprocessCore Version: 1.58.0 Imports: stats License: LGPL (>= 2) MD5sum: af17fa3ab71e94886d2f25295f7fff7a NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: RELEASE_3_15 git_last_commit: 2995e3e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/preprocessCore_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/preprocessCore_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/preprocessCore_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus, SCATE importsMe: affy, BloodGen3Module, bnbc, cn.farms, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PanomiR, PECA, PhosR, Pigengene, proBatch, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict, RAMClustR, retriever, SMDIC, WGCNA suggestsMe: DAPAR, MsCoreUtils, multiClust, QFeatures, scp, splatter, wateRmelon, aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.14.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: b5f9570beaea4511318d9def227134f2 NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: RELEASE_3_15 git_last_commit: 9d8ddfa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/primirTSS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/primirTSS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/primirTSS_1.14.0.tgz vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 194 Package: PrInCE Version: 1.12.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: 7ee2d354c581a1b9de43a67fc88c2605 NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: RELEASE_3_15 git_last_commit: 308cf69 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PrInCE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PrInCE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PrInCE_1.12.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 140 Package: proActiv Version: 1.6.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang, scales, S4Vectors, SummarizedExperiment, stats, tibble Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: cb82aafde2435babf9c3638fbfd7dc54 NeedsCompilation: no Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: RELEASE_3_15 git_last_commit: e817eaa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proActiv_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proActiv_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proActiv_1.6.0.tgz vignettes: vignettes/proActiv/inst/doc/proActiv.html vignetteTitles: Identifying Active and Alternative Promoters from RNA-Seq data with proActiv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/proActiv/inst/doc/proActiv.R dependencyCount: 124 Package: proBAMr Version: 1.30.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a28162f58016350f19fe3ef4432d15f9 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: RELEASE_3_15 git_last_commit: efcd524 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proBAMr_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proBAMr_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proBAMr_1.30.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 97 Package: proBatch Version: 1.11.0 Depends: R (>= 3.6) Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2, grDevices, lazyeval, lubridate, magrittr, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra, roxygen2, testthat (>= 2.1.0), spelling License: GPL-3 MD5sum: 412f1772200d483ef650bfda9ac1b392 NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry,Proteomics, QualityControl Author: Jelena Cuklina , Chloe H. Lee , Patrick Pedrioli Maintainer: Chloe H. Lee URL: https://github.com/symbioticMe/proBatch VignetteBuilder: knitr BugReports: https://github.com/symbioticMe/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: master git_last_commit: f9dca1a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proBatch_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proBatch_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proBatch_1.11.0.tgz vignettes: vignettes/proBatch/inst/doc/proBatch.pdf vignetteTitles: proBatch package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R dependencyCount: 163 Package: PROcess Version: 1.72.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: def68eaa7fdc03c9785d54c9e9700508 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: RELEASE_3_15 git_last_commit: dc06db4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PROcess_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROcess_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROcess_1.72.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.24.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: e3f392cb05c3a4f6e5772f94247f398e NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_15 git_last_commit: e90d156 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/procoil_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/procoil_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/procoil_2.24.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 30 Package: proDA Version: 1.10.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown License: GPL-3 MD5sum: c69912fac54ee566b5afc8f59eec30f3 NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (), Simon Anders [ths] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: RELEASE_3_15 git_last_commit: bd567b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proDA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proDA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proDA_1.10.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R importsMe: MatrixQCvis suggestsMe: protti dependencyCount: 27 Package: proFIA Version: 1.22.0 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL MD5sum: 507d43c86057b8b69846d23b232a8ac8 NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proFIA git_branch: RELEASE_3_15 git_last_commit: ecec2db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proFIA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proFIA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proFIA_1.22.0.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R dependsOnMe: plasFIA dependencyCount: 112 Package: profileplyr Version: 1.12.0 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices, rlang, tiff, Rsamtools Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Cairo License: GPL (>= 3) MD5sum: 2411f9f4761799ebede9ccbfc3598e3a NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: RELEASE_3_15 git_last_commit: cabfef2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/profileplyr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/profileplyr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/profileplyr_1.12.0.tgz vignettes: vignettes/profileplyr/inst/doc/profileplyr.html vignetteTitles: Visualization and annotation of read signal over genomic ranges with profileplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R dependencyCount: 209 Package: profileScoreDist Version: 1.24.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: x64 MD5sum: 69f1f32743adc4d54faf7b6f534239b3 NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: RELEASE_3_15 git_last_commit: 4895c00 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/profileScoreDist_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/profileScoreDist_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/profileScoreDist_1.24.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 6 Package: progeny Version: 1.18.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra, decoupleR, reshape2 Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: 1d884e9937fa08be14a25212283b3ab6 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (), Christian H. Holland [ctb] (), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_15 git_last_commit: 0219412 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/progeny_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/progeny_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/progeny_1.18.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: PROGENy pathway signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R importsMe: easier dependencyCount: 56 Package: projectR Version: 1.12.0 Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, reshape2, viridis, scales, ggplot2 Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap License: GPL (==2) Archs: x64 MD5sum: ab09fae19f96ae0f4d4d2eb299a58e45 NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: RELEASE_3_15 git_last_commit: 083c303 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/projectR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/projectR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/projectR_1.12.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.pdf vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 101 Package: pRoloc Version: 1.36.0 Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick License: GPL-2 MD5sum: 7c566c2c34f8a2a6ff35cc1f5af2d852 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_15 git_last_commit: 01d9bbf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pRoloc_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pRoloc_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pRoloc_1.36.0.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, Annotating spatial proteomics data, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: bandle, pRolocGUI suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 208 Package: pRolocGUI Version: 2.6.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, stats, grDevices, grid, BiocGenerics, shinydashboard Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 MD5sum: ff5a173c2622bd3ae63d83d2f79df5f6 NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut] (), Thomas Naake [aut], Laurent Gatto [aut, cre] () Maintainer: Laurent Gatto URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_15 git_last_commit: e912601 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pRolocGUI_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pRolocGUI_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pRolocGUI_2.6.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 222 Package: PROMISE Version: 1.48.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) Archs: x64 MD5sum: 8b911f12342767dc3dcbb57d3e67e1fb NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: RELEASE_3_15 git_last_commit: 6256d8c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PROMISE_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROMISE_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROMISE_1.48.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 50 Package: PROPER Version: 1.28.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: 5ae4ad6e38725cbee4951ef96fb9312d NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: RELEASE_3_15 git_last_commit: 94e3782 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PROPER_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROPER_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROPER_1.28.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R dependencyCount: 11 Package: PROPS Version: 1.18.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 97e772a342df311b290b9c42b0c8dd67 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: RELEASE_3_15 git_last_commit: 0829b35 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PROPS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROPS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROPS_1.18.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 76 Package: Prostar Version: 1.28.6 Depends: R (>= 4.1.0) Imports: DAPAR (>= 1.28.5), rhandsontable, data.table, shiny, shinyBS, shinyAce, highcharter, MSnbase, shinyWidgets, shinycssloaders, htmlwidgets, RColorBrewer, shinyjqui, later Suggests: BiocStyle, BiocManager, testthat, shinyTree, knitr, future, sass, R.utils, gplots, ggplot2, vioplot, promises, colourpicker, tibble, DAPARdata (>= 1.22.2), webshot, shinythemes, XML, gtools, compiler, shinyjs, DT License: Artistic-2.0 MD5sum: 9643a7839f98cb2a217d58da3c9c12d0 NeedsCompilation: no Title: A GUI for DAPAR package Description: This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, Software, GUI Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek [cre, aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_15 git_last_commit: d212650 git_last_commit_date: 2022-10-17 Date/Publication: 2022-10-18 source.ver: src/contrib/Prostar_1.28.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/Prostar_1.28.6.zip mac.binary.ver: bin/macosx/contrib/4.2/Prostar_1.28.6.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar User Manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R dependencyCount: 131 Package: proteinProfiles Version: 1.36.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 6fe50df9ab7f92828e8e466411b6210a NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: RELEASE_3_15 git_last_commit: 214a926 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proteinProfiles_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proteinProfiles_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proteinProfiles_1.36.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteoDisco Version: 1.2.0 Depends: R (>= 4.1.0), Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings (>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr (>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>= 1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0), methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6), rlang (>= 0.4.11), S4Vectors (>= 0.30.0), tibble (>= 3.1.2), tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>= 0.32.0), Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1), DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33), matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>= 3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>= 1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>= 1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>= 1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2) License: GPL-3 MD5sum: 027d429216c053d5ce123123ca5d7bcf NeedsCompilation: no Title: Generation of customized protein variant databases from genomic variants, splice-junctions and manual sequences Description: ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings. biocViews: Software, Proteomics, RNASeq, SNP, Sequencing, VariantAnnotation, DataImport Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de Werken [ths] Maintainer: Job van Riet URL: https://github.com/ErasmusMC-CCBC/ProteoDisco VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: RELEASE_3_15 git_last_commit: 80e3957 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ProteoDisco_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProteoDisco_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProteoDisco_1.2.0.tgz vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html vignetteTitles: Overview_Proteodisco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R dependencyCount: 106 Package: ProteoMM Version: 1.14.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 581a3cbada2dfc9fc8803162dc885984 NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: RELEASE_3_15 git_last_commit: 0e931ec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ProteoMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProteoMM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProteoMM_1.14.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R suggestsMe: mi4p dependencyCount: 92 Package: protGear Version: 1.0.0 Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>= 3.54.0) Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) , tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , kableExtra (>= 1.3.4), GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>= 2.4.0) Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) , ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>= 1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) , shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles (>= 0.9.1) , shinyFeedback (>= 0.3.0) License: GPL-3 MD5sum: 2e3527910586dc43fe7cedaa548642c2 NeedsCompilation: no Title: Protein Micro Array Data Management and Interactive Visualization Description: A generic three-step pre-processing package for protein microarray data. This package contains different data pre-processing procedures to allow comparison of their performance.These steps are background correction, the coefficient of variation (CV) based filtering, batch correction and normalization. biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni [ctb] Maintainer: Kennedy Mwai URL: https://github.com/Keniajin/protGear VignetteBuilder: knitr BugReports: https://github.com/Keniajin/protGear/issues git_url: https://git.bioconductor.org/packages/protGear git_branch: RELEASE_3_15 git_last_commit: f48850a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/protGear_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/protGear_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/protGear_1.0.0.tgz vignettes: vignettes/protGear/inst/doc/vignette.html vignetteTitles: protGear hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/protGear/inst/doc/vignette.R dependencyCount: 203 Package: ProtGenerics Version: 1.28.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 9750eafe90766553e6af7b49c45089c6 NeedsCompilation: no Title: Generic infrastructure for Bioconductor mass spectrometry packages Description: S4 generic functions and classes needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_15 git_last_commit: cfcd0a9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ProtGenerics_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProtGenerics_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProtGenerics_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, Spectra, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsFeatures, MSnID, mzID, mzR, PSMatch, QFeatures, xcms dependencyCount: 1 Package: PSEA Version: 1.30.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 3e83d85068e18c9953a47d98f01086ac NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/PSEA git_branch: RELEASE_3_15 git_last_commit: e5846d6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PSEA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PSEA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PSEA_1.30.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R dependencyCount: 8 Package: psichomics Version: 1.22.1 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: 181d55c5f66476a4d6c74efa21f64515 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (), Nuno Luís Barbosa-Morais [aut, led, ths] (), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_15 git_last_commit: 07747f4 git_last_commit_date: 2022-07-14 Date/Publication: 2022-07-17 source.ver: src/contrib/psichomics_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/psichomics_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/psichomics_1.22.1.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 206 Package: PSICQUIC Version: 1.34.0 Depends: R (>= 3.2.0), methods, IRanges, biomaRt (>= 2.34.1), BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 44c1a57ee3704cf8a19bf09d6a11a10a NeedsCompilation: no Title: Proteomics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_15 git_last_commit: f3c1547 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PSICQUIC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PSICQUIC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PSICQUIC_1.34.0.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependencyCount: 72 Package: PSMatch Version: 1.0.0 Depends: S4Vectors Imports: utils, stats, igraph, methods, Matrix, BiocParallel, BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils Suggests: msdata, rpx, mzID, mzR, Spectra, SummarizedExperiment, BiocStyle, rmarkdown, knitr, factoextra, testthat License: Artistic-2.0 Archs: x64 MD5sum: da21a69c60553d05019872b5d11169cf NeedsCompilation: no Title: Handling and Managing Peptide Spectrum Matches Description: The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto [aut, cre] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Samuel Wieczorek [ctb], Thomas Burger [ctb] Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/PSM VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PSM/issues git_url: https://git.bioconductor.org/packages/PSMatch git_branch: RELEASE_3_15 git_last_commit: 7cad135 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PSMatch_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PSMatch_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PSMatch_1.0.0.tgz vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html, vignettes/PSMatch/inst/doc/Fragments.html, vignettes/PSMatch/inst/doc/PSM.html vignetteTitles: Understanding protein groups with adjacency matrices, MS2 fragment ions, Working with PSM data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R, vignettes/PSMatch/inst/doc/Fragments.R, vignettes/PSMatch/inst/doc/PSM.R dependencyCount: 96 Package: psygenet2r Version: 1.28.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f85292d68dd8ba4d24185bcfd89ec1a6 NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/psygenet2r git_branch: RELEASE_3_15 git_last_commit: 1edaf6d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/psygenet2r_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/psygenet2r_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/psygenet2r_1.28.0.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R dependencyCount: 104 Package: ptairMS Version: 1.4.1 Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat, foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra, Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5, rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData, ropls License: GPL-3 MD5sum: 56625965d229fcef150f7a7eed37ab95 NeedsCompilation: yes Title: Pre-processing PTR-TOF-MS Data Description: This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a singl VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: RELEASE_3_15 git_last_commit: ce2af41 git_last_commit_date: 2022-06-30 Date/Publication: 2022-06-30 source.ver: src/contrib/ptairMS_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ptairMS_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ptairMS_1.4.1.tgz vignettes: vignettes/ptairMS/inst/doc/ptairMS.html vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R dependencyCount: 185 Package: PubScore Version: 1.8.0 Depends: R (>= 4.0.0) Imports: ggplot2, igraph, ggrepel,rentrez, progress, graphics, dplyr, utils, methods, intergraph, network, sna Suggests: FCBF, plotly, SummarizedExperiment, SingleCellExperiment, knitr, rmarkdown, testthat (>= 2.1.0), BiocManager, biomaRt License: MIT + file LICENSE MD5sum: 446f4785fb2182fda9fda823f055be73 NeedsCompilation: no Title: Automatic calculation of literature relevance of genes Description: Calculates the importance score for a given gene. The importance score is the total counts of articles in the pubmed database that are a result for that gene AND each term of a list. biocViews: GeneSetEnrichment, GeneExpression, SystemsBiology, Genetics, Epigenetics, BiomedicalInformatics, Visualization, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/PubScore git_branch: RELEASE_3_15 git_last_commit: caeb9dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PubScore_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PubScore_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PubScore_1.8.0.tgz vignettes: vignettes/PubScore/inst/doc/PubScore_vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PubScore/inst/doc/PubScore_vignette.R dependencyCount: 63 Package: pulsedSilac Version: 1.9.1 Depends: R (>= 3.6.0) Imports: robustbase, methods, R.utils, taRifx, S4Vectors, SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR, cowplot, grid, MuMIn Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra License: GPL-3 Archs: x64 MD5sum: bb28db2ee4a2c9419602892b7495308e NeedsCompilation: no Title: Analysis of pulsed-SILAC quantitative proteomics data Description: This package provides several tools for pulsed-SILAC data analysis. Functions are provided to organize the data, calculate isotope ratios, isotope fractions, model protein turnover, compare turnover models, estimate cell growth and estimate isotope recycling. Several visualization tools are also included to do basic data exploration, quality control, condition comparison, individual model inspection and model comparison. biocViews: Proteomics Author: Marc Pagès-Gallego, Tobias B. Dansen Maintainer: Marc Pagès-Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pulsedSilac git_branch: master git_last_commit: c375e7f git_last_commit_date: 2021-11-21 Date/Publication: 2021-11-21 source.ver: src/contrib/pulsedSilac_1.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/pulsedSilac_1.9.1.zip mac.binary.ver: bin/macosx/contrib/4.2/pulsedSilac_1.9.1.tgz vignettes: vignettes/pulsedSilac/inst/doc/pulsedsilac.html vignetteTitles: Pulsed-SILAC data analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pulsedSilac/inst/doc/pulsedsilac.R dependencyCount: 66 Package: puma Version: 3.38.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL MD5sum: 8b65d325b094a755bbe44d2ddf7eb5bf NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: RELEASE_3_15 git_last_commit: 93bfd22 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/puma_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/puma_3.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/puma_3.38.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 55 Package: PureCN Version: 2.2.0 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 MD5sum: 5bc2cf90e246ee1bdae3276fc102c9d8 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: RELEASE_3_15 git_last_commit: 008f1dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PureCN_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PureCN_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PureCN_2.2.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 121 Package: pvac Version: 1.44.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) Archs: x64 MD5sum: 1f062a4021dec7611ee8cbae23f64e8e NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: RELEASE_3_15 git_last_commit: 2ab5a18 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pvac_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pvac_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pvac_1.44.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 12 Package: pvca Version: 1.36.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: fc03f6f2f6c320f1344f942bf8625d09 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: RELEASE_3_15 git_last_commit: 38d0656 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pvca_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pvca_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pvca_1.36.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: proBatch, ExpressionNormalizationWorkflow, statVisual dependencyCount: 70 Package: Pviz Version: 1.30.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 6b0d1578ec0d8971978003e728378fc5 NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: RELEASE_3_15 git_last_commit: 805b735 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Pviz_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pviz_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pviz_1.30.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 146 Package: PWMEnrich Version: 4.32.0 Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 05248eaf2180afb3d9e88c4c668c9823 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_15 git_last_commit: 0e672b1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PWMEnrich_4.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PWMEnrich_4.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PWMEnrich_4.32.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 23 Package: pwOmics Version: 1.28.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: 4851e3c357de8ed9a5c9e667885a50a9 NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte git_url: https://git.bioconductor.org/packages/pwOmics git_branch: RELEASE_3_15 git_last_commit: cee2dac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pwOmics_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pwOmics_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pwOmics_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 115 Package: pwrEWAS Version: 1.10.0 Depends: shinyBS, foreach Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2, parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter, stats, grDevices, methods, utils, graphics, pwrEWAS.data Suggests: knitr, RUnit, BiocGenerics, rmarkdown License: Artistic-2.0 MD5sum: 223190fdff7594a948f22fb2675a4ae5 NeedsCompilation: no Title: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS) Description: pwrEWAS is a user-friendly tool to assists researchers in the design and planning of EWAS to help circumvent under- and overpowered studies. biocViews: DNAMethylation, Microarray, DifferentialMethylation, TissueMicroarray Author: Stefan Graw Maintainer: Stefan Graw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pwrEWAS git_branch: RELEASE_3_15 git_last_commit: 6c0aafb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/pwrEWAS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pwrEWAS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pwrEWAS_1.9.0.tgz vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf vignetteTitles: pwrEWAS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R dependencyCount: 124 Package: qckitfastq Version: 1.12.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 MD5sum: 0665fcb69ce8cc9fd39f4ff2bbfe128a NeedsCompilation: yes Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: RELEASE_3_15 git_last_commit: 8a67544 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qckitfastq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qckitfastq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qckitfastq_1.12.0.tgz vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf vignetteTitles: Quality control analysis and visualization using qckitfastq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R dependencyCount: 49 Package: qcmetrics Version: 1.34.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown License: GPL-2 MD5sum: 35eee0e93dbdb39eaa33ff022d0349d5 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: RELEASE_3_15 git_last_commit: 376656d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qcmetrics_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qcmetrics_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qcmetrics_1.34.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html vignetteTitles: Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R importsMe: MSstatsQC dependencyCount: 23 Package: QDNAseq Version: 1.32.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply (>= 1.8.1) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1), parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL Archs: x64 MD5sum: 1994ff67e30e85eef35b622074e0b3b7 NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: RELEASE_3_15 git_last_commit: aa8d720 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QDNAseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QDNAseq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QDNAseq_1.32.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, HiCcompare dependencyCount: 48 Package: QFeatures Version: 1.6.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.19.3), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.1.2), igraph, plotly Suggests: SingleCellExperiment, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: ec58a4128e05a92c932321c4f2681e06 NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (), Christophe Vanderaa [aut] () Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/QFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: RELEASE_3_15 git_last_commit: b79a83f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QFeatures_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QFeatures_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QFeatures_1.6.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, Data visualization from a QFeatures object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R, vignettes/QFeatures/inst/doc/Visualization.R dependsOnMe: msqrob2, scp, scpdata importsMe: MetaboAnnotation, PSMatch dependencyCount: 86 Package: qmtools Version: 1.0.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils, stats, igraph, VIM, scales, grDevices, graphics Suggests: limma, Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute, imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 3946d2ceda50988e298aac1fabc30111 NeedsCompilation: no Title: Quantitative Metabolomics Data Processing Tools Description: The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. Several functions in this package could also be used in other types of omics data. biocViews: Metabolomics, Preprocessing, Normalization, DimensionReduction, MassSpectrometry Author: Jaehyun Joo [aut, cre], Blanca Himes [aut] Maintainer: Jaehyun Joo URL: https://github.com/HimesGroup/qmtools VignetteBuilder: knitr BugReports: https://github.com/HimesGroup/qmtools/issues git_url: https://git.bioconductor.org/packages/qmtools git_branch: RELEASE_3_15 git_last_commit: 4de5611 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qmtools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qmtools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qmtools_1.0.0.tgz vignettes: vignettes/qmtools/inst/doc/qmtools.html vignetteTitles: Quantitative metabolomics data processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qmtools/inst/doc/qmtools.R dependencyCount: 154 Package: qpcrNorm Version: 1.54.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: d967a641ac8c9919cc11e7594b82d489 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: RELEASE_3_15 git_last_commit: db209eb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qpcrNorm_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qpcrNorm_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qpcrNorm_1.54.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R dependencyCount: 13 Package: qpgraph Version: 2.30.2 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.4-1), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) MD5sum: 19dab224d3c22adfbc456c1e388957a7 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: RELEASE_3_15 git_last_commit: e496c48 git_last_commit_date: 2022-09-20 Date/Publication: 2022-09-20 source.ver: src/contrib/qpgraph_2.30.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/qpgraph_2.30.2.zip mac.binary.ver: bin/macosx/contrib/4.2/qpgraph_2.30.2.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, simPATHy, topologyGSA dependencyCount: 103 Package: qPLEXanalyzer Version: 1.14.0 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: gridExtra, knitr, qPLEXdata, rmarkdown, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: fa923c2e6596a1dc7597272ac98f1e93 NeedsCompilation: no Title: Tools for qPLEX-RIME data analysis Description: Tools for quantitative proteomics data analysis generated from qPLEX-RIME method. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: RELEASE_3_15 git_last_commit: ed4ba40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qPLEXanalyzer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qPLEXanalyzer_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qPLEXanalyzer_1.14.0.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html vignetteTitles: qPLEXanalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependsOnMe: qPLEXdata dependencyCount: 103 Package: qrqc Version: 1.50.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rhtslib (>= 1.15.3) License: GPL (>=2) Archs: x64 MD5sum: 7bf62ae161d06636c1b51db405a68639 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/qrqc git_branch: RELEASE_3_15 git_last_commit: e6b52f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qrqc_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qrqc_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qrqc_1.50.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R dependencyCount: 162 Package: qsea Version: 1.22.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL (>=2) MD5sum: f86744108bde78ab3a2159bc0e6a49bf NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_15 git_last_commit: 3fff61a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qsea_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsea_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsea_1.22.0.tgz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R dependencyCount: 51 Package: qsmooth Version: 1.12.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 Archs: x64 MD5sum: 8130b2e075e91f15cc1564bd6368b5e7 NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: RELEASE_3_15 git_last_commit: 809351c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qsmooth_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsmooth_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsmooth_1.12.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R dependencyCount: 121 Package: QSutils Version: 1.14.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE Archs: x64 MD5sum: 542f7f78ea680615adb206f6d65382bd NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (), Josep Gregori i Font [aut] () Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: RELEASE_3_15 git_last_commit: 1f14029 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QSutils_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QSutils_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QSutils_1.14.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R dependencyCount: 26 Package: qsvaR Version: 1.0.0 Depends: R (>= 4.2), SummarizedExperiment Imports: sva, stats, ggplot2, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 5b29e4dec68691d9ffe43d597fef6d22 NeedsCompilation: no Title: Generate Quality Surrogate Variable Analysis for Degradation Correction Description: The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation. biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion, DifferentialExpression, Sequencing, Coverage Author: Joshua Stolz [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Joshua Stolz URL: https://github.com/LieberInstitute/qsvaR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/qsvaR git_url: https://git.bioconductor.org/packages/qsvaR git_branch: RELEASE_3_15 git_last_commit: 1c1adb1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qsvaR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsvaR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsvaR_1.0.0.tgz vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html vignetteTitles: Introduction to qsvaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R dependencyCount: 93 Package: Qtlizer Version: 1.10.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: cb2fe594ad02b4c99216e0c4a9bd381f NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: RELEASE_3_15 git_last_commit: a5b68ca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Qtlizer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Qtlizer_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Qtlizer_1.10.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 25 Package: quantiseqr Version: 1.4.1 Depends: R (>= 4.1.0) Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats, SummarizedExperiment, ggplot2, tidyr, rlang, utils Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery, knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat, tibble License: GPL-3 MD5sum: d3b47bc4d6c0f45e28d789f503eab255 NeedsCompilation: no Title: Quantification of the Tumor Immune contexture from RNA-seq data Description: This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. biocViews: GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray, Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification, StatisticalMethod, ExperimentHubSoftware, FlowCytometry Author: Federico Marini [aut, cre] (), Francesca Finotello [aut] () Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: RELEASE_3_15 git_last_commit: a1b3eb5 git_last_commit_date: 2022-09-27 Date/Publication: 2022-09-27 source.ver: src/contrib/quantiseqr_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantiseqr_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/quantiseqr_1.4.1.tgz vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html vignetteTitles: Using quantiseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R importsMe: easier dependencyCount: 64 Package: quantro Version: 1.30.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: cd5b36895e96d7a2983ffd33598ea1d8 NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: RELEASE_3_15 git_last_commit: e756c43 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/quantro_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantro_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/quantro_1.30.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: qsmooth dependencyCount: 153 Package: quantsmooth Version: 1.62.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 6b5a7b9573b3731ed5f2786f90db21fd NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: RELEASE_3_15 git_last_commit: b7c2752 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/quantsmooth_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantsmooth_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/quantsmooth_1.62.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 14 Package: QuartPAC Version: 1.28.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: ef007127b22fd16da01c7d43fe711ef1 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: RELEASE_3_15 git_last_commit: b200bd8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QuartPAC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuartPAC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuartPAC_1.28.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R dependencyCount: 43 Package: QuasR Version: 1.36.0 Depends: R (>= 4.1), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools, GenomicFeatures, ShortRead, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: a60f1294addef78842eb32ba9b7b20e5 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Charlotte Soneson [aut] (), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] () Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_15 git_last_commit: e0383d7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QuasR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuasR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuasR_1.36.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R importsMe: SingleMoleculeFootprinting suggestsMe: eisaR dependencyCount: 110 Package: QuaternaryProd Version: 1.30.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: 072ba099f83298c93016a3c4b3ec88ca NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: RELEASE_3_15 git_last_commit: 952a39a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QuaternaryProd_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuaternaryProd_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuaternaryProd_1.30.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 23 Package: QUBIC Version: 1.24.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE Archs: x64 MD5sum: f58165951b092a9993881e8163ff4c49 NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: RELEASE_3_15 git_last_commit: f838ca1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/QUBIC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QUBIC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QUBIC_1.24.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R importsMe: mosbi suggestsMe: runibic dependencyCount: 52 Package: qusage Version: 2.30.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) Archs: x64 MD5sum: 02cf3877d455f5c3c882771be5868a6c NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: RELEASE_3_15 git_last_commit: 8b51cf4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qusage_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qusage_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qusage_2.30.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R importsMe: mExplorer suggestsMe: NanoTube, SigCheck dependencyCount: 17 Package: qvalue Version: 2.28.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: x64 MD5sum: 2efcf971ca084b9c943617350f2a9fdc NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: RELEASE_3_15 git_last_commit: aaa62d5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/qvalue_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qvalue_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qvalue_2.28.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV, cp4p, isva importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge, epihet, erccdashboard, EventPointer, FindIT2, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RiboDiPA, RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch, subSeq, synapter, trigger, webbioc, IHWpaper, AEenrich, armada, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, medScan, NBPSeq, SeqFeatR, ssizeRNA suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, DGEobj.utils, easylabel, familiar, mutoss, Rediscover, seqgendiff, volcano3D, wrMisc dependencyCount: 42 Package: R3CPET Version: 1.28.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) MD5sum: 7d0c4ff27bf1ced97afaaef5bad644ba NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: RELEASE_3_15 git_last_commit: f45bc63 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/R3CPET_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R3CPET_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R3CPET_1.28.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 161 Package: r3Cseq Version: 1.42.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 MD5sum: 79956c5ff9948b58b7743ae4ab6af7ab NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: RELEASE_3_15 git_last_commit: c9c2f38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/r3Cseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/r3Cseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/r3Cseq_1.42.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 94 Package: R453Plus1Toolbox Version: 1.46.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: x64 MD5sum: 2893c71bf59eea463e67baed339376e8 NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: RELEASE_3_15 git_last_commit: 6f7b330 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/R453Plus1Toolbox_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R453Plus1Toolbox_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R453Plus1Toolbox_1.46.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 111 Package: R4RNA Version: 1.24.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: b32aace07344b846d64b98869d1b8967 NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: RELEASE_3_15 git_last_commit: 2eeebd3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/R4RNA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R4RNA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R4RNA_1.24.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R importsMe: ggmsa suggestsMe: rfaRm dependencyCount: 18 Package: RadioGx Version: 2.0.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander, markdown License: GPL-3 Archs: x64 MD5sum: 3d0d7f0e2673abe750cf985bbc5b0999 NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: RELEASE_3_15 git_last_commit: ec1fa52 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RadioGx_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RadioGx_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RadioGx_2.0.0.tgz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 139 Package: RaggedExperiment Version: 1.20.1 Depends: R (>= 3.6.0), GenomicRanges (>= 1.37.17) Imports: BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: 389883cfdb556345d1a78b62fa6b2cdc NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut, cre], Marcel Ramos [aut] () Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_15 git_last_commit: 062f316 git_last_commit_date: 2022-09-01 Date/Publication: 2022-09-04 source.ver: src/contrib/RaggedExperiment_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RaggedExperiment_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RaggedExperiment_1.20.1.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, compartmap importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils, terraTCGAdata suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, curatedTCGAData, SingleCellMultiModal dependencyCount: 25 Package: rain Version: 1.30.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 12954180082ea794ff643a59406c293b NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: RELEASE_3_15 git_last_commit: 01f8b9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rain_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rain_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rain_1.30.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 16 Package: rama Version: 1.70.0 Depends: R(>= 2.5.0) License: GPL (>= 2) MD5sum: 421042fcf29e0dbdc1e558484ee15087 NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/rama git_branch: RELEASE_3_15 git_last_commit: a9e0f02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rama_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rama_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rama_1.70.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge dependencyCount: 0 Package: ramr Version: 1.4.0 Depends: R (>= 4.1), GenomicRanges, parallel, doParallel, foreach, doRNG, methods Imports: IRanges, BiocGenerics, ggplot2, reshape2, EnvStats, ExtDist, matrixStats, S4Vectors Suggests: RUnit, knitr, rmarkdown, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: f69061334e2ce2d3a41841600b0382eb NeedsCompilation: no Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of low-frequency aberrant methylation events in large data sets obtained by methylation profiling using array or high-throughput bisulfite sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, MethylationArray, MethylSeq Author: Oleksii Nikolaienko [aut, cre] () Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: RELEASE_3_15 git_last_commit: 5cb93d3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ramr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ramr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ramr_1.4.0.tgz vignettes: vignettes/ramr/inst/doc/ramr.html vignetteTitles: ramr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramr/inst/doc/ramr.R dependencyCount: 66 Package: ramwas Version: 1.20.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 Archs: x64 MD5sum: d587a754f95705cb53c7f0e3adceb78b NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: RELEASE_3_15 git_last_commit: 2c1206f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ramwas_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ramwas_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ramwas_1.20.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 100 Package: RandomWalkRestartMH Version: 1.16.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: effc9293c84a8701f059b3ab20490a2b NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks" . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas [cre, aut, ctb] () Maintainer: Alberto Valdeolivas URL: https://github.com/alberto-valdeolivas/RandomWalkRestartMH VignetteBuilder: knitr BugReports: https://github.com/alberto-valdeolivas/RandomWalkRestartMH/issues git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_15 git_last_commit: 75cbf6b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RandomWalkRestartMH_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RandomWalkRestartMH_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RandomWalkRestartMH_1.16.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.html vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Network hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.R importsMe: netOmics dependencyCount: 54 Package: randPack Version: 1.42.0 Depends: methods Imports: Biobase License: Artistic 2.0 Archs: x64 MD5sum: d479e9490babe0b4ce5a06860ac8ba01 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: RELEASE_3_15 git_last_commit: 1cc66c6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/randPack_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/randPack_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/randPack_1.42.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 6 Package: randRotation Version: 1.8.0 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 MD5sum: 8b6e989eebd1d2bbf3a3ae5afa66b21b NeedsCompilation: no Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] () Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues git_url: https://git.bioconductor.org/packages/randRotation git_branch: RELEASE_3_15 git_last_commit: 872a3de git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/randRotation_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/randRotation_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/randRotation_1.8.0.tgz vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf vignetteTitles: Random Rotation Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R dependencyCount: 7 Package: RankProd Version: 3.22.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: f590c0b61fbd16658042d036d87d7847 NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: RELEASE_3_15 git_last_commit: e50beb9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RankProd_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RankProd_3.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RankProd_3.22.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: tRanslatome importsMe: POMA, synlet, INCATome dependencyCount: 6 Package: RAREsim Version: 1.0.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 4f9c5d69a8eeff2357b24c5b4cedfe73 NeedsCompilation: no Title: Simulation of Rare Variant Genetic Data Description: Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation. biocViews: Genetics, Software, VariantAnnotation, Sequencing Author: Megan Null [aut], Ryan Barnard [cre] Maintainer: Ryan Barnard URL: https://github.com/meganmichelle/RAREsim VignetteBuilder: knitr BugReports: https://github.com/meganmichelle/RAREsim/issues git_url: https://git.bioconductor.org/packages/RAREsim git_branch: RELEASE_3_15 git_last_commit: 82c5861 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/RAREsim_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RAREsim_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RAREsim_1.0.0.tgz vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html vignetteTitles: RAREsim Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R dependencyCount: 38 Package: RareVariantVis Version: 2.24.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: e7e085f3051b133ea44d8852ae6ca36a NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: RELEASE_3_15 git_last_commit: 94a6f09 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RareVariantVis_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RareVariantVis_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RareVariantVis_2.24.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 131 Package: rawrr Version: 1.4.0 Depends: R (>= 4.1) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 5ec016bdc69d54b21d876b504b1fa6b8 NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the RawFileReader .NET assembly. Within the R environment, spectra and chromatograms are represented by S3 objects (Kockmann T. et al. (2020) ). The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland . biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (), Tobias Kockmann [aut] () Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: RELEASE_3_15 git_last_commit: 3f84950 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rawrr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rawrr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rawrr_1.4.0.tgz vignettes: vignettes/rawrr/inst/doc/rawrr.html vignetteTitles: Direct Access to Orbitrap Data and Beyond hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawrr/inst/doc/rawrr.R importsMe: MsBackendRawFileReader dependencyCount: 4 Package: RbcBook1 Version: 1.64.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 8703dd9844be532b9e7b2f564b0abf60 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: RELEASE_3_15 git_last_commit: bc251c0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RbcBook1_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RbcBook1_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RbcBook1_1.64.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 10 Package: Rbec Version: 1.4.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 191b92222549b9d39f170b2a37d39146 NeedsCompilation: yes Title: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities Description: Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples. biocViews: Sequencing, MicrobialStrain, Microbiome Author: Pengfan Zhang [aut, cre] Maintainer: Pengfan Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: RELEASE_3_15 git_last_commit: 4728ade git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rbec_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbec_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbec_1.4.0.tgz vignettes: vignettes/Rbec/inst/doc/Rbec.html vignetteTitles: Rbec hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbec/inst/doc/Rbec.R dependencyCount: 97 Package: RBGL Version: 1.72.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ae0c0acec6cb758c4d1104900aece828 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_15 git_last_commit: a86f310 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RBGL_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBGL_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBGL_1.72.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, pkgDepTools, PerfMeas, RSeed, SubpathwayLNCE importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GAPGOM, GenomicInteractionNodes, GOSim, GOstats, MIGSA, NCIgraph, OrganismDbi, pkgDepTools, RpsiXML, Streamer, VariantFiltering, BiDAG, bnClustOmics, eff2, gRbase, HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, wiseR suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, gRc, maGUI dependencyCount: 8 Package: RBioinf Version: 1.56.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: c3c63b748a48680eaef76809bbcf4258 NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/RBioinf git_branch: RELEASE_3_15 git_last_commit: bca20b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RBioinf_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBioinf_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBioinf_1.56.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R dependencyCount: 7 Package: rBiopaxParser Version: 2.36.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) Archs: x64 MD5sum: b6f4055c9f960a681358901af97ea267 NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: RELEASE_3_15 git_last_commit: f0ea375 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rBiopaxParser_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rBiopaxParser_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rBiopaxParser_2.36.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R importsMe: pwOmics suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: RBM Version: 1.28.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) Archs: x64 MD5sum: a596f4048e58f5e86acddb594f797d8d NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: RELEASE_3_15 git_last_commit: c66fb16 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RBM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBM_1.28.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 7 Package: Rbowtie Version: 1.36.0 Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 86813624330159c7b4e22c7f7dd326c8 NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler URL: https://github.com/fmicompbio/Rbowtie SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rbowtie/issues git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: RELEASE_3_15 git_last_commit: d579bff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rbowtie_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbowtie_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbowtie_1.36.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: crisprBowtie, MACPET, multicrispr suggestsMe: eisaR dependencyCount: 0 Package: Rbowtie2 Version: 2.2.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) MD5sum: c9683d9e92631d951aa2b00f953a9866 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei [aut, cre], Wei Zhang [aut] Maintainer: Zheng Wei SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_15 git_last_commit: c82ce47 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rbowtie2_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbowtie2_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbowtie2_2.2.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: esATAC, UMI4Cats dependencyCount: 31 Package: rbsurv Version: 2.54.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) Archs: x64 MD5sum: ed14a12d16c010ed991ef2d2880df7d7 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: RELEASE_3_15 git_last_commit: 5aca619 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rbsurv_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rbsurv_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rbsurv_2.54.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 12 Package: Rbwa Version: 1.0.0 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + LICENSE OS_type: unix MD5sum: a8003dd92a21852aac962917510c81f9 NeedsCompilation: yes Title: R wrapper for BWA-backtrack and BWA-MEM aligners Description: Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines. biocViews: Sequencing, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/Rbwa SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/Rbwa/issues git_url: https://git.bioconductor.org/packages/Rbwa git_branch: RELEASE_3_15 git_last_commit: f83ea6c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rbwa_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Rbwa_1.0.0.tgz vignettes: vignettes/Rbwa/inst/doc/Rbwa.html vignetteTitles: An introduction to Rbwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R importsMe: crisprBwa dependencyCount: 0 Package: Rcade Version: 1.37.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>= 0.23.19), IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 9bc0cfe1f1f8ca6e996b69e932995226 NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns git_url: https://git.bioconductor.org/packages/Rcade git_branch: master git_last_commit: 564d8e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/Rcade_1.37.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Rcade_1.37.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R dependencyCount: 65 Package: RCAS Version: 1.22.0 Depends: R (>= 3.5.0), plotly (>= 4.5.2), DT (>= 0.2), data.table Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, ggseqlogo, utils, ranger, gprofiler2 Suggests: testthat, covr License: Artistic-2.0 Archs: x64 MD5sum: 958a449cd5a0f61187f2627fc9facfe0 NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: RELEASE_3_15 git_last_commit: 3841e30 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCAS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCAS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCAS_1.22.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R dependencyCount: 156 Package: RCASPAR Version: 1.42.0 License: GPL (>=3) MD5sum: a3d76f41471707fd9a26cd27ac03a7ed NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: RELEASE_3_15 git_last_commit: 2180718 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCASPAR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCASPAR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCASPAR_1.42.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.18.0 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel, rmarkdown License: LGPL-3 + file LICENSE MD5sum: b6ec5897fe5a455f63874fb35a0ba8a7 NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: RELEASE_3_15 git_last_commit: c69b494 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rcellminer_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rcellminer_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rcellminer_2.18.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 70 Package: rCGH Version: 1.26.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: fab610484507bd7cf210d1e22ca2ca11 NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: RELEASE_3_15 git_last_commit: 2b9e639 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rCGH_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rCGH_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rCGH_1.26.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R importsMe: preciseTAD suggestsMe: excluderanges dependencyCount: 141 Package: RcisTarget Version: 1.16.0 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, gplots, rtracklayer, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: 03b375645dffb3cc1e03bffdcb26a64f NeedsCompilation: no Title: RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: RELEASE_3_15 git_last_commit: d0bacf0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RcisTarget_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RcisTarget_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RcisTarget_1.16.0.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment, RcisTarget - on regions, RcisTarget - with background hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R dependencyCount: 102 Package: RCM Version: 1.12.0 Depends: R (>= 4.0), DBI Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 091ef1c6253ecce54cfb85d4a368c22c NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: RELEASE_3_15 git_last_commit: d055a6f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCM_1.12.0.tgz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 92 Package: Rcpi Version: 1.32.2 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 | file LICENSE MD5sum: 49ef1d6c325e7329bdf1a7268e571ed9 NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_15 git_last_commit: 4c237e4 git_last_commit_date: 2022-07-17 Date/Publication: 2022-07-19 source.ver: src/contrib/Rcpi_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rcpi_1.32.2.zip mac.binary.ver: bin/macosx/contrib/4.2/Rcpi_1.32.2.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 58 Package: RCSL Version: 1.4.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats Suggests: knitr, rmarkdown, mclust, RcppAnnoy License: GPL-3 MD5sum: ac6d821339df5391dadce112820a0f80 NeedsCompilation: no Title: Rank Constrained Similarity Learning for single cell RNA sequencing data Description: A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Sequencing Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd] Maintainer: Qinglin Mei URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: RELEASE_3_15 git_last_commit: 67c4725 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCSL_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCSL_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCSL_1.4.0.tgz vignettes: vignettes/RCSL/inst/doc/RCSL.html vignetteTitles: RCSL package manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCSL/inst/doc/RCSL.R dependencyCount: 55 Package: Rcwl Version: 1.12.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools, basilisk Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: 4967624c84a701cd6960e07b20c0fed7 NeedsCompilation: no Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: RELEASE_3_15 git_last_commit: de7dc5e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rcwl_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Rcwl_1.12.0.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: Rcwl: An R interface to the Common Workflow Language hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines dependencyCount: 117 Package: RcwlPipelines Version: 1.12.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 7d02a8ec416cb001852be5b2800a6552 NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: RELEASE_3_15 git_last_commit: 4c0644c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RcwlPipelines_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/RcwlPipelines_1.12.0.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: RcwlPipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R dependencyCount: 131 Package: RCX Version: 1.0.1 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE MD5sum: 93a900917c1c686fa9315c94c5cccab6 NeedsCompilation: no Title: R package implementing the Cytoscape Exchange (CX) format Description: Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape. biocViews: Pathways, DataImport, Network Author: Florian Auer [aut, cre] () Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/RCX VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/RCX/issues git_url: https://git.bioconductor.org/packages/RCX git_branch: RELEASE_3_15 git_last_commit: 95d04a4 git_last_commit_date: 2022-10-05 Date/Publication: 2022-10-06 source.ver: src/contrib/RCX_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCX_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RCX_1.0.1.tgz vignettes: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX from scratch, 03. Extending the RCX Data Model, 01. RCX - an R package implementing the Cytoscape Exchange (CX) format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R dependsOnMe: ndexr dependencyCount: 15 Package: RCy3 Version: 2.16.0 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, uchardet, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE MD5sum: 6af097d214d1dbbc991f6dbe768b38c7 NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_15 git_last_commit: 9488419 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCy3_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCy3_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCy3_2.16.0.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14. Jupyter Bridge and RCy3 ~10 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, fedup, MOGAMUN, NCIgraph, netZooR, regutools, TimiRGeN, transomics2cytoscape, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, netDx, rScudo, sharp, sparsebnUtils dependencyCount: 45 Package: RCyjs Version: 2.18.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4cece20b0a0360addb0830e10c7a0f56 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_15 git_last_commit: 90ee158 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RCyjs_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCyjs_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCyjs_2.18.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 17 Package: rDGIdb Version: 1.22.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: c39de559e4b8494b7ef7f3da5f342e10 NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (https://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Lars Bosshard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rDGIdb git_branch: RELEASE_3_15 git_last_commit: dae9000 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rDGIdb_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rDGIdb_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rDGIdb_1.22.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R dependencyCount: 11 Package: Rdisop Version: 1.56.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 8ecf8ad75f64394d27e21ce10d5f2329 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: RELEASE_3_15 git_last_commit: 826d9b1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rdisop_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rdisop_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rdisop_1.56.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: CorMID, enviGCMS, HiResTEC, InterpretMSSpectrum suggestsMe: adductomicsR, MSnbase, RforProteomics dependencyCount: 3 Package: RDRToolbox Version: 1.46.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 55d7f5fd1558bd6b70284c33a9c1bf75 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: RELEASE_3_15 git_last_commit: 970437c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RDRToolbox_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RDRToolbox_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RDRToolbox_1.46.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon dependencyCount: 26 Package: ReactomeContentService4R Version: 1.4.0 Imports: httr, jsonlite, utils, magick (>= 2.5.1), data.table, doParallel, foreach, parallel Suggests: pdftools, testthat, knitr, rmarkdown License: Apache License (>= 2.0) | file LICENSE Archs: x64 MD5sum: dd8635336e3315e7b227a130ea0df903 NeedsCompilation: no Title: Interface for the Reactome Content Service Description: Reactome is a free, open-source, open access, curated and peer-reviewed knowledgebase of bio-molecular pathways. This package is to interact with the Reactome Content Service API. Pre-built functions would allow users to retrieve data and images that consist of proteins, pathways, and other molecules related to a specific gene or entity in Reactome. biocViews: DataImport, Pathways, Reactome Author: Chi-Lam Poon [aut, cre] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeContentService4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeContentService4R/issues git_url: https://git.bioconductor.org/packages/ReactomeContentService4R git_branch: RELEASE_3_15 git_last_commit: fa809a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/ReactomeContentService4R_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeContentService4R_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeContentService4R_1.4.0.tgz vignettes: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.html vignetteTitles: ReactomeContentService4R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.R importsMe: ReactomeGraph4R dependencyCount: 20 Package: ReactomeGraph4R Version: 1.4.0 Depends: R (>= 4.1) Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table, rlang, ReactomeContentService4R, doParallel, parallel, foreach Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork, wesanderson License: Apache License (>= 2) MD5sum: 5b1e0ddb9f3d06e27c744d038dd2e34d NeedsCompilation: no Title: Interface for the Reactome Graph Database Description: Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries. biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork Author: Chi-Lam Poon [aut, cre] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeGraph4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGraph4R/issues git_url: https://git.bioconductor.org/packages/ReactomeGraph4R git_branch: RELEASE_3_15 git_last_commit: b7209cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReactomeGraph4R_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeGraph4R_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeGraph4R_1.4.0.tgz vignettes: vignettes/ReactomeGraph4R/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomeGraph4R/inst/doc/Introduction.R dependencyCount: 70 Package: ReactomeGSA Version: 1.10.0 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer, dplyr, tidyr Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE MD5sum: cafb62127e680a8f6254b51202db2043 NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: RELEASE_3_15 git_last_commit: d582814 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReactomeGSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeGSA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeGSA_1.10.0.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data dependencyCount: 60 Package: ReactomePA Version: 1.40.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 486b846e6f4bfc53d4b56040dd156bcd NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: RELEASE_3_15 git_last_commit: dac7445 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReactomePA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomePA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomePA_1.40.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R dependsOnMe: maEndToEnd importsMe: bioCancer, epihet, miRspongeR, multiSight, Pigengene, scTensor, ExpHunterSuite suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, GRaNIE, scGPS dependencyCount: 129 Package: ReadqPCR Version: 1.42.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: c3520a2195e2e93fd87f10dc0ddbe1c0 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: RELEASE_3_15 git_last_commit: fbeaf03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReadqPCR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReadqPCR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReadqPCR_1.42.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 6 Package: REBET Version: 1.14.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 2d9dfb3de11b2a2e9284325a5ed83fad NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: RELEASE_3_15 git_last_commit: d9e4dc9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/REBET_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/REBET_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/REBET_1.14.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 16 Package: rebook Version: 1.6.0 Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends, dir.expiry, filelock, BiocStyle Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown, rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows License: GPL-3 MD5sum: 0af89e74d546f7c54930f3e99c25463a NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: RELEASE_3_15 git_last_commit: 00b7660 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rebook_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rebook_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rebook_1.6.0.tgz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependsOnMe: csawBook, OSCA, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows suggestsMe: SingleRBook dependencyCount: 42 Package: receptLoss Version: 1.8.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE Archs: x64 MD5sum: 73d18bc343f5630669d419b20af31ed2 NeedsCompilation: no Title: Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors Description: receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well. biocViews: GeneExpression, StatisticalMethod Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: RELEASE_3_15 git_last_commit: 694d3ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-08-11 source.ver: src/contrib/receptLoss_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/receptLoss_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/receptLoss_1.8.0.tgz vignettes: vignettes/receptLoss/inst/doc/receptLoss.html vignetteTitles: receptLoss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R dependencyCount: 60 Package: reconsi Version: 1.8.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 1b40dfe13d8183d4766ca29903eb52a0 NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_15 git_last_commit: d992460 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/reconsi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/reconsi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/reconsi_1.8.0.tgz vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R dependencyCount: 88 Package: recount Version: 1.22.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT, edgeR, ggplot2, RColorBrewer License: Artistic-2.0 MD5sum: 10f2bb842b685ff7dca5c0d363c8dc54 NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (), Kasper Daniel Hansen [ctb] (), Ben Langmead [ctb] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_15 git_last_commit: 514a4a1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/recount_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/recount_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/recount_1.22.0.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: dasper, ODER, recount3 dependencyCount: 164 Package: recount3 Version: 1.6.0 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, RCurl, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, interactiveDisplayBase, recount License: Artistic-2.0 MD5sum: 8e3ce86d37e890ebcc9a0ced4f481291 NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] () Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: RELEASE_3_15 git_last_commit: 32292b2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/recount3_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/recount3_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/recount3_1.6.0.tgz vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R suggestsMe: RNAseqQC dependencyCount: 89 Package: recountmethylation Version: 1.6.1 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, IlluminaHumanMethylation450kmanifest Suggests: knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: 47a70cad223f90e3707ffd5e3c94d87c NeedsCompilation: no Title: Access and analyze public DNA methylation array data compilations Description: Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (), Brian Walsh [aut] (), Kyle Ellrott [aut] (), Kasper D Hansen [aut] (), Reid F Thompson [aut] (), Abhinav Nellore [aut] () Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues git_url: https://git.bioconductor.org/packages/recountmethylation git_branch: RELEASE_3_15 git_last_commit: 6b0db13 git_last_commit_date: 2022-05-28 Date/Publication: 2022-05-29 source.ver: src/contrib/recountmethylation_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/recountmethylation_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/recountmethylation_1.6.1.tgz vignettes: vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html, vignettes/recountmethylation/inst/doc/recountmethylation_glint.html, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.html, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.html, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Data Analyses, Determine population ancestry from DNAm arrays, Power analysis for DNAm arrays, Nearest neighbors analysis for DNAm arrays, recountmethylation User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R, vignettes/recountmethylation/inst/doc/recountmethylation_glint.R, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.R, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.R, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R dependencyCount: 143 Package: recoup Version: 1.24.0 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, Biostrings, circlize, GenomeInfoDb, GenomicFeatures, graphics, grDevices, httr, IRanges, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, utils Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: 06a8bc72d96c509fc7b38d0d7384b943 NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: RELEASE_3_15 git_last_commit: 945741c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/recoup_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/recoup_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/recoup_1.24.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 121 Package: RedeR Version: 2.0.1 Depends: R (>= 4.0), methods Imports: igraph Suggests: BiocStyle, knitr, rmarkdown, markdown, TreeAndLeaf License: GPL (>= 3) MD5sum: b527307e408d04ca7bd9ab86cb6614e5 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (Java>= 11) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_15 git_last_commit: 9e1b175 git_last_commit_date: 2022-08-26 Date/Publication: 2022-08-28 source.ver: src/contrib/RedeR_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RedeR_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RedeR_2.0.1.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical networks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf dependencyCount: 12 Package: REDseq Version: 1.42.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) MD5sum: f3735c2dff60f30c01e2d21dd0f7f437 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: RELEASE_3_15 git_last_commit: fd21558 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/REDseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/REDseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/REDseq_1.42.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 125 Package: RefPlus Version: 1.66.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 2af9121f3b44228b3f0da9a539313c4b NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang git_url: https://git.bioconductor.org/packages/RefPlus git_branch: RELEASE_3_15 git_last_commit: 1a67d40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RefPlus_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RefPlus_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RefPlus_1.66.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R dependencyCount: 26 Package: RegEnrich Version: 1.6.0 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: 8f30003a4e6ad3328581f0b4a097c142 NeedsCompilation: no Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: RELEASE_3_15 git_last_commit: be39426 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RegEnrich_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RegEnrich_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RegEnrich_1.6.0.tgz vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html vignetteTitles: Gene regulator enrichment with RegEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R dependencyCount: 150 Package: regioneR Version: 1.28.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 8142a2637e7ab8beb48ac8cc64764d9c NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: RELEASE_3_15 git_last_commit: 65a4cb0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/regioneR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regioneR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regioneR_1.28.0.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RIPAT, RLSeq, UMI4Cats suggestsMe: CNVRanger, MitoHEAR dependencyCount: 50 Package: regionReport Version: 1.30.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: 1d8cddec60184ee6b300fad77ce11c3c NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_15 git_last_commit: e0de84e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/regionReport_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regionReport_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regionReport_1.30.0.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 175 Package: regsplice Version: 1.22.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: c5189bca696f1fd80b871e2b82996545 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: RELEASE_3_15 git_last_commit: c19a3ac git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/regsplice_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regsplice_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regsplice_1.22.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 39 Package: regutools Version: 1.8.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: bdc9e1fa3614a8b92ee1f497318a0ca0 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (), Carmina Barberena-Jonas [aut] (), Jesus E. Sotelo-Fonseca [aut] (), Jose Alquicira-Hernandez [ctb] (), Heladia Salgado [ctb] (), Leonardo Collado-Torres [aut] (), Alejandro Reyes [aut] () Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: RELEASE_3_15 git_last_commit: 65b3988 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/regutools_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regutools_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regutools_1.8.0.tgz vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 174 Package: REMP Version: 1.20.1 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: faae3566b221415760f18fcf209c8c71 NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_15 git_last_commit: 58547b6 git_last_commit_date: 2022-05-13 Date/Publication: 2022-05-15 source.ver: src/contrib/REMP_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/REMP_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/REMP_1.20.1.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 207 Package: Repitools Version: 1.42.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) MD5sum: 8b16aa25a7dcf4b795bc2b1a2ab02299 NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: RELEASE_3_15 git_last_commit: 9936214 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Repitools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Repitools_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Repitools_1.42.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R dependencyCount: 116 Package: ReportingTools Version: 2.36.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db, rmarkdown, markdown License: Artistic-2.0 MD5sum: 85b2f432b94a64e0ffdbf72e82aa928c NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_15 git_last_commit: 34122d4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReportingTools_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReportingTools_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReportingTools_2.36.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R dependsOnMe: rnaseqGene importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 177 Package: RepViz Version: 1.12.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: rmarkdown, knitr, testthat License: GPL-3 Archs: x64 MD5sum: c0e7deb0e1b4c7e0bec67b0e867cbf4e NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: RELEASE_3_15 git_last_commit: 9ccdc5c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RepViz_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RepViz_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RepViz_1.12.0.tgz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 83 Package: ReQON Version: 1.42.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: 4890d8e504759d4fdf67f0a1bcb10086 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 git_url: https://git.bioconductor.org/packages/ReQON git_branch: RELEASE_3_15 git_last_commit: 32776bc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ReQON_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReQON_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReQON_1.42.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R dependencyCount: 32 Package: ResidualMatrix Version: 1.6.1 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 64e34f0aad04071827d8d5339f06c94d NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: RELEASE_3_15 git_last_commit: 5a36715 git_last_commit_date: 2022-08-16 Date/Publication: 2022-08-16 source.ver: src/contrib/ResidualMatrix_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ResidualMatrix_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ResidualMatrix_1.6.1.tgz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: BiocSingular, scran dependencyCount: 15 Package: restfulSE Version: 1.18.0 Depends: R (>= 3.6), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle, restfulSEData, rmarkdown License: Artistic-2.0 MD5sum: 9968e81a52a4da1c5e254827457a29f8 NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_15 git_last_commit: bd7e725 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/restfulSE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/restfulSE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/restfulSE_1.18.0.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn dependencyCount: 109 Package: rexposome Version: 1.18.2 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: bbe7ea02ab771b8b98afe7df634083e2 NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: RELEASE_3_15 git_last_commit: 9afec1f git_last_commit_date: 2022-06-27 Date/Publication: 2022-06-28 source.ver: src/contrib/rexposome_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/rexposome_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/rexposome_1.18.2.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome suggestsMe: brgedata dependencyCount: 156 Package: rfaRm Version: 1.8.0 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics License: GPL-3 MD5sum: d799db5a14fae2c4aaee962e8e5f91eb NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: RELEASE_3_15 git_last_commit: 078e398 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rfaRm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rfaRm_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rfaRm_1.8.0.tgz vignettes: vignettes/rfaRm/inst/doc/rfaRm.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R dependencyCount: 47 Package: Rfastp Version: 1.6.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 6ae8745903f8d8b492015886b45fce79 NeedsCompilation: yes Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [aut] (), Ji-Dung Luo [ctb] (), Thomas Carroll [cre, aut] () Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: RELEASE_3_15 git_last_commit: d1713e7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rfastp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rfastp_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rfastp_1.6.0.tgz vignettes: vignettes/Rfastp/inst/doc/Rfastp.html vignetteTitles: Rfastp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R dependencyCount: 45 Package: rfPred Version: 1.34.2 Depends: R (>= 3.5.0), methods Imports: utils, GenomeInfoDb, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) MD5sum: 5fb5c99048f200567a67da93f3e15e6e NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_15 git_last_commit: 1c52425 git_last_commit_date: 2022-10-04 Date/Publication: 2022-10-04 source.ver: src/contrib/rfPred_1.34.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/rfPred_1.34.2.zip mac.binary.ver: bin/macosx/contrib/4.2/rfPred_1.34.2.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 31 Package: rGADEM Version: 2.44.1 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 Archs: x64 MD5sum: a7d82dcb1102f8e7e1b423703303630d NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit git_url: https://git.bioconductor.org/packages/rGADEM git_branch: RELEASE_3_15 git_last_commit: 10675d1 git_last_commit_date: 2022-09-13 Date/Publication: 2022-09-13 source.ver: src/contrib/rGADEM_2.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGADEM_2.44.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rGADEM_2.44.1.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: TCGAWorkflow dependencyCount: 47 Package: rGenomeTracks Version: 1.2.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 0b121492bc77a35f76f9c119a5208408 NeedsCompilation: no Title: Integerated visualization of epigenomic data Description: rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users. biocViews: Software, HiC, Visualization Author: Omar Elashkar [aut, cre] () Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: RELEASE_3_15 git_last_commit: 06593df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rGenomeTracks_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGenomeTracks_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rGenomeTracks_1.2.0.tgz vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html vignetteTitles: rGenomeTracks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R dependencyCount: 103 Package: Rgin Version: 1.15.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.5.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 81e167d31a45461e5920a8bb16f0897d NeedsCompilation: yes Title: gin in R Description: C++ implementation of SConES. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm [aut], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rgin git_branch: master git_last_commit: 2f19288 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rgin_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rgin_1.15.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rgin_1.15.0.tgz vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html vignetteTitles: Using Rgin C++ libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 10 Package: RGMQL Version: 1.16.0 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 11f8706c06dba718f1ed27ad6e0cedc8 NeedsCompilation: no Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, ImmunoOncology, SingleCell Author: Simone Pallotta [aut, cre], Marco Masseroli [aut] Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: RELEASE_3_15 git_last_commit: 4f9e75c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RGMQL_1.16.0.tar.gz vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.html vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R dependencyCount: 74 Package: rgoslin Version: 1.0.99 Imports: Rcpp (>= 1.0.3), dplyr LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra, BiocManager, stringr, ggplot2, tibble, lipidr License: MIT + file LICENSE MD5sum: e2ce1c60b85b84f7eb2070791094874f NeedsCompilation: yes Title: Lipid Shorthand Name Parsing and Normalization Description: The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable. biocViews: Software, Lipidomics, Metabolomics, Preprocessing, Normalization, MassSpectrometry Author: Nils Hoffmann [aut, cre] (), Dominik Kopczynski [aut] () Maintainer: Nils Hoffmann URL: https://github.com/lifs-tools/rgoslin VignetteBuilder: knitr BugReports: https://github.com/lifs-tools/rgoslin/issues git_url: https://git.bioconductor.org/packages/rgoslin git_branch: RELEASE_3_15 git_last_commit: b7d7eff git_last_commit_date: 2022-10-16 Date/Publication: 2022-10-18 source.ver: src/contrib/rgoslin_1.0.99.tar.gz win.binary.ver: bin/windows/contrib/4.2/rgoslin_1.0.99.zip mac.binary.ver: bin/macosx/contrib/4.2/rgoslin_1.0.99.tgz vignettes: vignettes/rgoslin/inst/doc/introduction.html vignetteTitles: Using R Goslin to parse and normalize lipid nomenclature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rgoslin/inst/doc/introduction.R dependencyCount: 22 Package: RGraph2js Version: 1.24.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 Archs: x64 MD5sum: f66b759ea1c4c370d8f16c164690bc63 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: RELEASE_3_15 git_last_commit: 030a0fa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RGraph2js_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RGraph2js_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RGraph2js_1.24.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 10 Package: Rgraphviz Version: 2.40.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: x64 MD5sum: 5879c2926dca7dfcadf1530fa4aa052b NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: RELEASE_3_15 git_last_commit: d864c97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rgraphviz_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rgraphviz_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rgraphviz_2.40.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, maEndToEnd, dlsem, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, MIGSA, mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, SplicingGraphs, trackViewer, TRONCO, abn, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet, gRain, gRbase, gRim, hpoPlot, ontologyPlot, SEMgraph, stablespec, wiseR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, OmnipathR, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RpsiXML, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnclassify, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, gRc, HEMDAG, iTOP, kpcalg, kst, lava, loon, maGUI, MCDA, micd, migraph, multiplex, ParallelPC, pcalg, psych, relations, rEMM, rPref, RSeed, rSpectral, SCCI, sisal, textplot, tm, topologyGSA, unifDAG, zenplots dependencyCount: 9 Package: rGREAT Version: 1.28.0 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats Suggests: testthat (>= 0.3), knitr, circlize (>= 0.4.8), rmarkdown License: MIT + file LICENSE MD5sum: 6f35ff42a8c70d4c99cfccfe21d1b427 NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: RELEASE_3_15 git_last_commit: df57cc8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rGREAT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGREAT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rGREAT_1.28.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: Analyze with GREAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R suggestsMe: TADCompare dependencyCount: 21 Package: RGSEA Version: 1.30.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) Archs: x64 MD5sum: d2ee392005eadbf76ed3c4fe83900b26 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: RELEASE_3_15 git_last_commit: 4e5d4c8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RGSEA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RGSEA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RGSEA_1.30.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 5 Package: rgsepd Version: 1.28.0 Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 3de63c856f45f169d71f7d69ba036da3 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_15 git_last_commit: 4ad0ddc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rgsepd_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rgsepd_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rgsepd_1.28.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 128 Package: rhdf5 Version: 2.40.0 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2, mockery License: Artistic-2.0 MD5sum: baa5d6e79005622452df8ab7099586b6 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut, cre] (), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: RELEASE_3_15 git_last_commit: fb6c15a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rhdf5_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5_2.40.0.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, HDF5Array, HiCBricks, LoomExperiment, MuData importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HiCcompare, IONiseR, MOFA2, NxtIRFcore, phantasus, ptairMS, PureCN, recountmethylation, ribor, scCB2, scone, signatureSearch, trackViewer, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, DmelSGI, MethylSeqData, ptairData, signatureSearchData, bioRad, file2meco, NEONiso, ondisc, rDataPipeline, smapr suggestsMe: edgeR, rhdf5filters, SCArray, slalom, Spectra, SummarizedExperiment, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, digitalDLSorteR, io, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX dependencyCount: 3 Package: rhdf5client Version: 1.18.0 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 MD5sum: ecfef404e708a727e4554118d37314de NeedsCompilation: yes Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_15 git_last_commit: cc97b23 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rhdf5client_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5client_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5client_1.18.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: restfulSE suggestsMe: BiocOncoTK, HumanTranscriptomeCompendium dependencyCount: 25 Package: rhdf5filters Version: 1.8.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), rhdf5 (>= 2.34.0) License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 92ab1e5a8bbd8c8d797f78f26e07e564 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of compression filters for use with HDF5 datasets. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, cre] () Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5filters git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: RELEASE_3_15 git_last_commit: b0b588b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rhdf5filters_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5filters_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5filters_1.8.0.tgz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: HDF5Array, rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.18.2 Depends: R (>= 4.0.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 Archs: x64 MD5sum: 7446628fe0802a80799b358a7d025da2 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb, cre] (), The HDF Group [cph] Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: RELEASE_3_15 git_last_commit: d104bbf git_last_commit_date: 2022-05-12 Date/Publication: 2022-05-15 source.ver: src/contrib/Rhdf5lib_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhdf5lib_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhdf5lib_1.18.2.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: epigraHMM, rhdf5 suggestsMe: mbkmeans linksToMe: CytoML, DropletUtils, epigraHMM, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, ondisc dependencyCount: 0 Package: Rhisat2 Version: 1.12.0 Depends: R (>= 3.6) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: e178cf7cef95543b71e04e373e86c175 NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: RELEASE_3_15 git_last_commit: 8a93852 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rhisat2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhisat2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhisat2_1.12.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R suggestsMe: eisaR, QuasR dependencyCount: 100 Package: Rhtslib Version: 1.28.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) Archs: x64 MD5sum: 68bbb8477444f2c8e7bbf7720ece120f NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.7 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: RELEASE_3_15 git_last_commit: 214fde2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rhtslib_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhtslib_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhtslib_1.28.0.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, epialleleR, FLAMES, h5vc, maftools, methylKit, mitoClone2, podkat, qrqc, QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead, TransView, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboCrypt Version: 1.2.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: BiocGenerics, BiocParallel, Biostrings, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, plotly, rlang Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Archs: x64 MD5sum: a1fb1c01762923c2fafa158bded60e0c NeedsCompilation: no Title: Interactive visualization in genomics Description: R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development. biocViews: Software, Sequencing, RiboSeq, RNASeq, Author: Michal Swirski [aut, cre], Haakon Tjeldnes [ctb] Maintainer: Michal Swirski URL: https://github.com/m-swirski/RiboCrypt VignetteBuilder: knitr BugReports: https://github.com/m-swirski/RiboCrypt/issues git_url: https://git.bioconductor.org/packages/RiboCrypt git_branch: RELEASE_3_15 git_last_commit: d80a3e1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RiboCrypt_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboCrypt_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboCrypt_1.2.0.tgz vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html vignetteTitles: RiboCrypt_overview.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R dependencyCount: 149 Package: RiboDiPA Version: 1.4.1 Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism, methods, S4Vectors, IRanges, GenomicRanges, matrixStats, reldist, doParallel, foreach, parallel, qvalue, DESeq2, ggplot2, BiocFileCache,BiocGenerics LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) Archs: x64 MD5sum: cb942ae6ea847bfd95d5791bce4e509d NeedsCompilation: yes Title: Differential pattern analysis for Ribo-seq data Description: This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization. biocViews: RiboSeq, GeneExpression, GeneRegulation, DifferentialExpression, Sequencing, Coverage, Alignment, RNASeq, ImmunoOncology, QualityControl, DataImport, Software, Normalization Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping Wang [aut, cre] Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: RELEASE_3_15 git_last_commit: 6817f37 git_last_commit_date: 2022-06-25 Date/Publication: 2022-06-26 source.ver: src/contrib/RiboDiPA_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboDiPA_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboDiPA_1.4.1.tgz vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html vignetteTitles: RiboDiPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R dependencyCount: 145 Package: RiboProfiling Version: 1.26.0 Depends: R (>= 3.5.0), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 MD5sum: 29ead0ad21e5f2b0d16bad6603a7510b NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: RELEASE_3_15 git_last_commit: 9f71470 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RiboProfiling_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboProfiling_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboProfiling_1.26.0.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 161 Package: ribor Version: 1.8.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 9c594e91ae472f0c15438abc817c7320 NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: RELEASE_3_15 git_last_commit: c38bfe1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ribor_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ribor_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ribor_1.8.0.tgz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 52 Package: riboSeqR Version: 1.30.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 8d71ff0075e872514264a3f7acda5f40 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_15 git_last_commit: 482e24f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/riboSeqR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/riboSeqR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/riboSeqR_1.30.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 39 Package: ribosomeProfilingQC Version: 1.8.0 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, edgeR, limma, testthat, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 6b6a16a407fe6d99c987516693689777 NeedsCompilation: no Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: RELEASE_3_15 git_last_commit: 887822a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ribosomeProfilingQC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ribosomeProfilingQC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ribosomeProfilingQC_1.8.0.tgz vignettes: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html vignetteTitles: ribosomeProfilingQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R dependencyCount: 160 Package: rifi Version: 1.0.0 Depends: R (>= 4.1) Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, graphics, grDevices, grid, methods, nls2, nnet, rlang, S4Vectors, scales, stats, stringr, SummarizedExperiment, tibble, rtracklayer, utils Suggests: DescTools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: 78b26224da219e1a0de1ca808cdf484b NeedsCompilation: no Title: 'rifi' anyalyses data from rifampicin time series ceated by microarray or RNAseq Description: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Regression, Microarray, Software Author: Jens Georg [aut, cre] Maintainer: Jens Georg VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: RELEASE_3_15 git_last_commit: a495442 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/rifi_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/rifi_1.0.0.tgz vignettes: vignettes/rifi/inst/doc/vignette.html vignetteTitles: Rifi for decay estimation,, based on high resolution microarray or RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifi/inst/doc/vignette.R dependencyCount: 129 Package: RImmPort Version: 1.24.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 6b927b47c016317db24c564170555e18 NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: RELEASE_3_15 git_last_commit: 0bc562c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RImmPort_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RImmPort_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RImmPort_1.24.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 41 Package: Ringo Version: 1.60.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 MD5sum: eeb4de0e477436e5f44996d738686e73 NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/Ringo git_branch: RELEASE_3_15 git_last_commit: 8dd9329 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Ringo_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Ringo_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Ringo_1.60.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, ccTutorial importsMe: Repitools dependencyCount: 81 Package: RIPAT Version: 1.6.0 Depends: R (>= 4.0) Imports: biomaRt (>= 2.38.0), GenomicRanges (>= 1.34.0), ggplot2 (>= 3.1.0), grDevices (>= 3.5.3), IRanges (>= 2.16.0), karyoploteR (>= 1.6.3), openxlsx (>= 4.1.4), plyr (>= 1.8.4), regioneR (>= 1.12.0), rtracklayer (>= 1.42.2), stats (>= 3.5.3), stringr (>= 1.3.1), utils (>= 3.5.3) Suggests: knitr (>= 1.28) License: Artistic-2.0 MD5sum: bc5a90f796e851bae859160208b09cd0 NeedsCompilation: no Title: Retroviral Integration Pattern Analysis Tool (RIPAT) Description: RIPAT is developed as an R package for retroviral integration sites annotation and distribution analysis. RIPAT needs local alignment results from BLAST and BLAT. Specific input format is depicted in RIPAT manual. RIPAT provides RV integration pattern analysis result as forms of R objects, excel file with multiple sheets and plots. biocViews: Annotation Author: Min-Jeong Baek [aut, cre] Maintainer: Min-Jeong Baek URL: https://github.com/bioinfo16/RIPAT/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RIPAT git_branch: RELEASE_3_15 git_last_commit: 68c07aa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RIPAT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RIPAT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RIPAT_1.6.0.tgz vignettes: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.html vignetteTitles: RIPAT : Retroviral Integration Pattern Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.R dependencyCount: 152 Package: Risa Version: 1.38.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL Archs: x64 MD5sum: a7dbdd901d98e72582f9ed5164402ac4 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues git_url: https://git.bioconductor.org/packages/Risa git_branch: RELEASE_3_15 git_last_commit: 9a02a6a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Risa_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Risa_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Risa_1.38.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R suggestsMe: mtbls2 dependencyCount: 96 Package: RITAN Version: 1.20.0 Depends: R (>= 4.0), Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86 Suggests: rmarkdown, BgeeDB License: file LICENSE MD5sum: 2bfbb0b52064de8e7758240e1664b6a5 NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics, GraphAndNetwork Author: Michael Zimmermann [aut, cre] Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_15 git_last_commit: 3d2e50b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RITAN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RITAN_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RITAN_1.20.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 151 Package: RIVER Version: 1.20.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: bc3996a3afb0799a151aaa3cea1fa8bc NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: RELEASE_3_15 git_last_commit: eaf1fbe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RIVER_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RIVER_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RIVER_1.20.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 48 Package: RJMCMCNucleosomes Version: 1.20.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: b4a116ed578238ab95142a628b8b00e3 NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: RELEASE_3_15 git_last_commit: 7e5b59f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RJMCMCNucleosomes_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RJMCMCNucleosomes_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RJMCMCNucleosomes_1.20.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 51 Package: RLassoCox Version: 1.4.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 MD5sum: 65b868c90d471367a87a0a308337d964 NeedsCompilation: no Title: A reweighted Lasso-Cox by integrating gene interaction information Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. biocViews: Survival, Regression, GeneExpression, GenePrediction, Network Author: Wei Liu [cre, aut] () Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: RELEASE_3_15 git_last_commit: 882d596 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RLassoCox_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLassoCox_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RLassoCox_1.4.0.tgz vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf vignetteTitles: RLassoCox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R dependencyCount: 21 Package: RLMM Version: 1.58.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) Archs: x64 MD5sum: 0a163adcb348b15f7e5cef3c543d0796 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: RELEASE_3_15 git_last_commit: 820942c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RLMM_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLMM_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RLMM_1.58.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: RLSeq Version: 1.2.0 Depends: R (>= 4.1.0) Imports: dplyr, ggplot2, RColorBrewer, grid, regioneR, valr, caretEnsemble, GenomicFeatures, rtracklayer, GenomicRanges, GenomeInfoDb, ComplexHeatmap, AnnotationHub, VennDiagram, callr, circlize, ggplotify, ggprism, methods, stats, RLHub, aws.s3, pheatmap Suggests: AnnotationDbi, BiocStyle, covr, lintr, rcmdcheck, DT, httr, jsonlite, kableExtra, kernlab, knitr, magick, MASS, org.Hs.eg.db, R.utils, randomForest, readr, rmarkdown, rpart, testthat (>= 3.0.0), tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, futile.logger License: MIT + file LICENSE MD5sum: 26cf5d34023015e70c3a5b5d537e0075 NeedsCompilation: no Title: RLSeq: An analysis package for R-loop mapping data Description: RLSeq is a toolkit for analyzing and evaluating R-loop mapping datasets. RLSeq serves two primary purposes: (1) to facilitate the evaluation of dataset quality, and (2) to enable R-loop analysis in the context of publicly-available data sets from RLBase. The package is intended to provide a simple pipeline, called with the `RLSeq()` function, which performs all main analyses. Individual functions are also accessible and provide custom analysis capabilities. Finally an HTML report is generated with `report()`. biocViews: Sequencing, Coverage, Epigenetics, Transcriptomics, Classification Author: Henry Miller [aut, cre, cph] (), Daniel Montemayor [ctb] (), Simon Levy [ctb] (), Anna Vines [ctb] (), Alexander Bishop [ths, cph] () Maintainer: Henry Miller URL: https://github.com/Bishop-Laboratory/RLSeq, https://bishop-laboratory.github.io/RLSeq/ VignetteBuilder: knitr BugReports: https://github.com/Bishop-Laboratory/RLSeq/issues git_url: https://git.bioconductor.org/packages/RLSeq git_branch: RELEASE_3_15 git_last_commit: 7afa0ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RLSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RLSeq_1.2.0.tgz vignettes: vignettes/RLSeq/inst/doc/RLSeq.html vignetteTitles: Analyzing R-loop Data with RLSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RLSeq/inst/doc/RLSeq.R dependencyCount: 199 Package: Rmagpie Version: 1.52.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: ea3c23f71d8c0da3247fd3980b5aa96b NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: RELEASE_3_15 git_last_commit: 143fee8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rmagpie_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rmagpie_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rmagpie_1.52.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 19 Package: RMassBank Version: 3.6.1 Depends: Rcpp Imports: XML,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat,logger,RCurl,readJDX,webchem, ChemmineR,ChemmineOB,R.utils,data.table Suggests: BiocStyle,gplots,RMassBankData (>= 1.33.1), xcms (>= 1.37.1), CAMERA, RUnit, knitr, rmarkdown License: Artistic-2.0 MD5sum: 67ec388b5d5df29ad2db876acf8ad16e NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_15 git_last_commit: 2fa5677 git_last_commit_date: 2022-05-31 Date/Publication: 2022-05-31 source.ver: src/contrib/RMassBank_3.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RMassBank_3.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RMassBank_3.6.1.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html, vignettes/RMassBank/inst/doc/RMassBankXCMS.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage, RMassBank for XCMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R suggestsMe: RMassBankData dependencyCount: 127 Package: rmelting Version: 1.12.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.5-0) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: 37a55c98cfeb7cdd7daf9fac8950f524 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels-static/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/rmelting/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: RELEASE_3_15 git_last_commit: 445fded git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rmelting_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/rmelting_1.12.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.14.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2a04d41cc2424d8074c39ad5b8d403a6 NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: RELEASE_3_15 git_last_commit: 6c903b0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rmmquant_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rmmquant_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rmmquant_1.14.0.tgz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 185 Package: rmspc Version: 1.2.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: f442d1d54b13d6017c1ef43e4339401f NeedsCompilation: no Title: Multiple Sample Peak Calling Description: The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples. biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre] Maintainer: Meriem Bahda URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 5.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: RELEASE_3_15 git_last_commit: d711bca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rmspc_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rmspc_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rmspc_1.2.0.tgz vignettes: vignettes/rmspc/inst/doc/rmpsc.html vignetteTitles: User guide to the rmspc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rmspc/inst/doc/rmpsc.R dependencyCount: 53 Package: RNAAgeCalc Version: 1.8.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 4cbf09a34fed6ebc41ae638c69a5bf2f NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: RELEASE_3_15 git_last_commit: c1f2c7e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAAgeCalc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAAgeCalc_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAAgeCalc_1.8.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 167 Package: RNAdecay Version: 1.16.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 88346660189b37735faeab1bf41a5846 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: RELEASE_3_15 git_last_commit: ae6bfe5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAdecay_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAdecay_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAdecay_1.16.0.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 63 Package: rnaEditr Version: 1.6.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: ff47601a7a6bee0fb89542669c1c8cd2 NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: RELEASE_3_15 git_last_commit: 388a2fe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rnaEditr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rnaEditr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rnaEditr_1.6.0.tgz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 129 Package: RNAinteract Version: 1.44.0 Depends: R (>= 2.12.0), Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12), abind, locfit, Biobase License: Artistic-2.0 Archs: x64 MD5sum: 39b2e1c9e816b1c92bc6df7380b6703f NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer [aut], Wolfgang Huber [ctb], Mike Smith [cre] Maintainer: Mike Smith git_url: https://git.bioconductor.org/packages/RNAinteract git_branch: RELEASE_3_15 git_last_commit: 943ec11 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAinteract_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAinteract_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAinteract_1.44.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R dependsOnMe: RNAinteractMAPK dependencyCount: 108 Package: RNAmodR Version: 1.10.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 MD5sum: 31838f8791eec65556ab73c8af025011 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: RELEASE_3_15 git_last_commit: ad86538 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAmodR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR_1.10.0.tgz vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 155 Package: RNAmodR.AlkAnilineSeq Version: 1.10.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, RNAmodR.Data License: Artistic-2.0 MD5sum: cc1c97a847fa4da0d2cb4d83fdcb1331 NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: RELEASE_3_15 git_last_commit: fedd3c5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.AlkAnilineSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.AlkAnilineSeq_1.10.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 156 Package: RNAmodR.ML Version: 1.10.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: eba32484a31597e4313707dbda5d02fd NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: RELEASE_3_15 git_last_commit: e1fc653 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAmodR.ML_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.ML_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.ML_1.10.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 157 Package: RNAmodR.RiboMethSeq Version: 1.10.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, RNAmodR.Data License: Artistic-2.0 MD5sum: f66e383dd38a9a6cb32f4df785d76e35 NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: RELEASE_3_15 git_last_commit: 22e1cca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.RiboMethSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.RiboMethSeq_1.10.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 156 Package: RNAsense Version: 1.10.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: be733d6b4d09f564ffade6a62f04cf9e NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: RELEASE_3_15 git_last_commit: 36c13f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNAsense_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAsense_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAsense_1.10.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 61 Package: rnaseqcomp Version: 1.26.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 417391b7ff23de63f7acf771e91f9f15 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: RELEASE_3_15 git_last_commit: 4b50c39 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rnaseqcomp_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rnaseqcomp_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rnaseqcomp_1.26.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R suggestsMe: SummarizedBenchmark dependencyCount: 2 Package: RNASeqPower Version: 1.36.0 License: LGPL (>=2) MD5sum: e60d73cded3f77334e3a379b57c166e4 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: RELEASE_3_15 git_last_commit: 4df9c71 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RNASeqPower_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNASeqPower_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNASeqPower_1.36.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R suggestsMe: DGEobj.utils dependencyCount: 0 Package: RnaSeqSampleSize Version: 2.6.0 Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, Rcpp (>= 0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 7d275ba77332a1c8b7ef579396331e29 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo [aut], Quanhu Sheng [aut], Yu Shyr [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_15 git_last_commit: b738cd4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RnaSeqSampleSize_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RnaSeqSampleSize_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RnaSeqSampleSize_2.6.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 206 Package: RnBeads Version: 2.14.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, grid, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19, RnBeads.mm9, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame License: GPL-3 MD5sum: 3dcac32ff2b2aa182a5e04ccea5d7de8 NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: RELEASE_3_15 git_last_commit: 2543e25 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RnBeads_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RnBeads_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RnBeads_2.14.0.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R dependsOnMe: MAGAR suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5 dependencyCount: 169 Package: Rnits Version: 1.29.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 Archs: x64 MD5sum: 24a71a594ac12748631a6437bef3d364 NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: master git_last_commit: f913d43 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rnits_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rnits_1.29.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rnits_1.29.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 53 Package: roar Version: 1.32.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: c50dfbb9cede46f15a828e534ba32d7e NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: RELEASE_3_15 git_last_commit: 1fc9a85 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/roar_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/roar_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/roar_1.32.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R dependencyCount: 45 Package: ROC Version: 1.72.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase License: Artistic-2.0 MD5sum: c20e17a9d0ff3cca15e648377be172ad NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: RELEASE_3_15 git_last_commit: c5d083b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ROC_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROC_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROC_1.72.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst, rMisbeta suggestsMe: genefilter dependencyCount: 13 Package: ROCpAI Version: 1.8.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: b2827b3b5ae9e4186d0fdb16d94b4033 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: RELEASE_3_15 git_last_commit: 21b775f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ROCpAI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROCpAI_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROCpAI_1.8.0.tgz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 36 Package: RolDE Version: 1.0.0 Depends: R (>= 4.2.0) Imports: stats, methods, ROTS, matrixStats, foreach, parallel, doParallel, doRNG, rngtools, SummarizedExperiment, nlme, qvalue, grDevices, graphics, utils Suggests: knitr, printr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 78aa6695defadadffa3be9301a58d2dd NeedsCompilation: no Title: RolDE: Robust longitudinal Differential Expression Description: RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings. biocViews: StatisticalMethod, Software, TimeCourse, Regression, Proteomics, DifferentialExpression Author: Tommi Valikangas [cre, aut] Maintainer: Tommi Valikangas URL: https://github.com/elolab/RolDE VignetteBuilder: knitr BugReports: https://github.com/elolab/RolDE/issues git_url: https://git.bioconductor.org/packages/RolDE git_branch: RELEASE_3_15 git_last_commit: 7a0f055 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RolDE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RolDE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RolDE_1.0.0.tgz vignettes: vignettes/RolDE/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RolDE/inst/doc/Introduction.R dependencyCount: 67 Package: rols Version: 2.24.4 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: b8793bb3314e86f9332e618aa2c69b9a NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre], Tiage Chedraoui Silva [ctb], Andrew Clugston [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_15 git_last_commit: ccfb801 git_last_commit_date: 2022-08-31 Date/Publication: 2022-09-01 source.ver: src/contrib/rols_2.24.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/rols_2.24.4.zip mac.binary.ver: bin/macosx/contrib/4.2/rols_2.24.4.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R importsMe: spatialHeatmap, struct suggestsMe: MSnbase, RforProteomics dependencyCount: 27 Package: ROntoTools Version: 2.24.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: d59111d672a6f92eef8cf19abd96d831 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Calin Voichita git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_15 git_last_commit: 2ab8ea9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ROntoTools_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROntoTools_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROntoTools_2.24.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA dependencyCount: 34 Package: ropls Version: 1.28.2 Depends: R (>= 3.5.0) Imports: Biobase, graphics, grDevices, methods, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 98bffd0441e10ca142d1300e4d3986ae NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot [aut, cre] () Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_15 git_last_commit: 8c9616b git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/ropls_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ropls_1.28.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ropls_1.28.2.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: ropls-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R importsMe: ASICS, biosigner, lipidr, MultiBaC, proFIA suggestsMe: autonomics, ptairMS, structToolbox, MetabolomicsBasics dependencyCount: 67 Package: ROSeq Version: 1.8.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 8deec589d3e530631ff10b207da2df7f NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: RELEASE_3_15 git_last_commit: 57c7bf2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ROSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROSeq_1.8.0.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 1.24.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: d49f037f25574a76b5f53a139420fca9 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_15 git_last_commit: 372e462 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ROTS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROTS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROTS_1.24.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, RolDE suggestsMe: wrProteo dependencyCount: 7 Package: RPA Version: 1.52.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 92a2c56f27cb6e13f5a55448eae5a9ef NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] () Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: RELEASE_3_15 git_last_commit: feedfed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RPA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RPA_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RPA_1.52.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 99 Package: rprimer Version: 1.0.0 Depends: R (>= 4.2) Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods, patchwork, reshape2, S4Vectors, shiny, shinycssloaders, shinyFeedback Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: ae58176e8b749b77c4dff7090e5e557c NeedsCompilation: no Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment Description: Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette. biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment, SequenceMatching, qPCR Author: Sofia Persson [aut, cre] () Maintainer: Sofia Persson URL: https://github.com/sofpn/rprimer VignetteBuilder: knitr BugReports: https://github.com/sofpn/rprimer/issues git_url: https://git.bioconductor.org/packages/rprimer git_branch: RELEASE_3_15 git_last_commit: 1feeaed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rprimer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rprimer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rprimer_1.0.0.tgz vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html vignetteTitles: Instructions for use hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R dependencyCount: 84 Package: RProtoBufLib Version: 2.8.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: 0238540715486ff802bdc942a385f4fd NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: RELEASE_3_15 git_last_commit: bc46096 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RProtoBufLib_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RProtoBufLib_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RProtoBufLib_2.8.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: RpsiXML Version: 2.38.0 Depends: methods, XML (>= 2.4.0), utils Imports: annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, Rgraphviz, ppiStats, ScISI, testthat License: LGPL-3 MD5sum: 53149cdc45b82ccf9689442a89c8a889 NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang [aut, cre, ctb] (), Stefan Wiemann [ctb], Marc Carlson [ctb], Tony Chiang [ctb] Maintainer: Jitao David Zhang URL: http://www.bioconductor.org PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/RpsiXML git_branch: RELEASE_3_15 git_last_commit: c2e1b0f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RpsiXML_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RpsiXML_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RpsiXML_2.38.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependencyCount: 52 Package: rpx Version: 2.4.1 Depends: methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 69cd08cd1e5b1e2a069b1ce5a4b79637 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: RELEASE_3_15 git_last_commit: 8270697 git_last_commit_date: 2022-08-10 Date/Publication: 2022-08-11 source.ver: src/contrib/rpx_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rpx_2.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rpx_2.4.1.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MSnbase, PSMatch, RforProteomics dependencyCount: 48 Package: Rqc Version: 1.30.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: a27793c24c299760d9e4284c71b61c18 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: RELEASE_3_15 git_last_commit: 28967ba git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rqc_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rqc_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rqc_1.30.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 168 Package: rqt Version: 1.22.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: acf79d2a98e0da9251296015a680b602 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_15 git_last_commit: c4e0d3a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rqt_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rqt_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rqt_1.22.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 137 Package: rqubic Version: 1.42.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: x64 MD5sum: dac1544354cb346ef311d30939af4231 NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] () Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: RELEASE_3_15 git_last_commit: d21bb02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rqubic_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rqubic_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rqubic_1.42.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM suggestsMe: RcmdrPlugin.BiclustGUI dependencyCount: 52 Package: rRDP Version: 1.30.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: 71ec99bdffd11ea90da08c297f141325 NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_15 git_last_commit: 2e7e530 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rRDP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rRDP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rRDP_1.30.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 18 Package: RRHO Version: 1.36.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 Archs: x64 MD5sum: 4b813ac161ae959d22ef23ef466159f5 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: RELEASE_3_15 git_last_commit: df7abb7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RRHO_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RRHO_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RRHO_1.36.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 8 Package: rrvgo Version: 1.8.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: c328156f07164abec0a9034ec815df0d NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: RELEASE_3_15 git_last_commit: de1ac17 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rrvgo_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rrvgo_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rrvgo_1.8.0.tgz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R suggestsMe: genekitr dependencyCount: 101 Package: Rsamtools Version: 2.12.0 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6), R (>= 3.5.0) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 1.17.7), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE MD5sum: 0ae178e7e6673b879c99adeab5efcc1e NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Hervé Pagès, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_15 git_last_commit: d6a65dd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rsamtools_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rsamtools_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rsamtools_2.12.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf vignetteTitles: An introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: ArrayExpressHTS, BitSeq, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, FRASER, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook, Brundle importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, atena, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics, BSgenome, CAGEr, casper, cellbaseR, CexoR, cfDNAPro, ChIC, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromstaR, chromVAR, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEXSeq, DiffBind, diffHic, easyRNASeq, EDASeq, ensembldb, epigenomix, epigraHMM, eudysbiome, extraChIPs, FilterFFPE, FLAMES, FunChIP, gcapc, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylKit, MMAPPR2, mosaics, motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICS, plyranges, pram, profileplyr, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rbowtie2, recoup, Repitools, rfPred, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, Rqc, rtracklayer, scDblFinder, scruff, segmentSeq, seqsetvis, SimFFPE, single, sitadela, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, chipseqDBData, LungCancerLines, MMAPPR2data, ggcoverage, hoardeR, intePareto, kibior, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, scPloidy, Signac, spp, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, IRanges, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, systemPipeRdata, chipseqDB, polyRAD, seqmagick dependencyCount: 29 Package: rsbml Version: 2.54.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: 6359bc3b8d376cc370f8e0d7006aca3d NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: RELEASE_3_15 git_last_commit: f1b961c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rsbml_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rsbml_2.54.0.zip vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR, seeds dependencyCount: 7 Package: rScudo Version: 1.12.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 Archs: x64 MD5sum: 91e13f0c6d563fc7596603a114c75c19 NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: RELEASE_3_15 git_last_commit: face4cf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rScudo_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rScudo_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rScudo_1.12.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 32 Package: rsemmed Version: 1.6.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 1205dff8cc97b2ac3c0d64f709a5546e NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] () Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: RELEASE_3_15 git_last_commit: 6525cb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rsemmed_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rsemmed_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rsemmed_1.6.0.tgz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 28 Package: RSeqAn Version: 1.16.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: c06d95fcf73fd2b9371ca0e71cff5066 NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: RELEASE_3_15 git_last_commit: f3409bb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RSeqAn_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RSeqAn_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RSeqAn_1.16.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R importsMe: qckitfastq linksToMe: qckitfastq dependencyCount: 3 Package: Rsubread Version: 2.10.5 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: fbdb89baccbdec67c77d3f795b2a8eb2 NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_15 git_last_commit: 120893f git_last_commit_date: 2022-08-08 Date/Publication: 2022-08-09 source.ver: src/contrib/Rsubread_2.10.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rsubread_2.10.5.zip mac.binary.ver: bin/macosx/contrib/4.2/Rsubread_2.10.5.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, diffUTR, dupRadar, FRASER, ribosomeProfilingQC, scruff suggestsMe: autonomics, icetea, NxtIRFcore, scPipe, singleCellTK, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.36.0 Depends: R (>= 3.5.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: 68dfc65fe7ae9e6f2b7a1429bb1f38b7 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: RELEASE_3_15 git_last_commit: d483427 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RSVSim_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RSVSim_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RSVSim_1.36.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 50 Package: rSWeeP Version: 1.8.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 96d4da47bb9c9eba735cc16eab98440b NeedsCompilation: no Title: Functions to creation of low dimensional comparative matrices of Amino Acid Sequence occurrences Description: The SWeeP method was developed to favor the analizes between amino acids sequences and to assist alignment free phylogenetic studies. This method is based on the concept of sparse words, which is applied in the scan of biological sequences and its the conversion in a matrix of ocurrences. Aiming the generation of low dimensional matrices of Amino Acid Sequence occurrences. biocViews: Software,StatisticalMethod,SupportVectorMachine,Technology,Sequencing,Genetics, Alignment Author: Danrley R. Fernandes [com, cre, aut] Maintainer: Danrley R. Fernandes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: RELEASE_3_15 git_last_commit: 0aa9146 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rSWeeP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rSWeeP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rSWeeP_1.8.0.tgz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 5 Package: RTCA Version: 1.48.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: e8386316b9d40022ca0541ededd3988a NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: RELEASE_3_15 git_last_commit: a328f4e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTCA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCA_1.48.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 8 Package: RTCGA Version: 1.26.0 Depends: R (>= 3.3.0) Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales Suggests: devtools, testthat, pander, rmarkdown, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr License: GPL-2 MD5sum: ff1ed72ecb339790d98f87bcb111498b NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq Author: Marcin Kosinski , Przemyslaw Biecek Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: RELEASE_3_15 git_last_commit: 952c633 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTCGA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCGA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCGA_1.26.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA dependencyCount: 125 Package: RTCGAToolbox Version: 2.26.1 Depends: R (>= 3.5.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, limma, methods, RaggedExperiment, RCircos, RCurl, RJSONIO, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, survival, TCGAutils (>= 1.9.4), XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: file LICENSE MD5sum: 561c0d2b23bd076a56a78fbad2e7b33a NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre], Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: RELEASE_3_15 git_last_commit: bd76ae2 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-13 source.ver: src/contrib/RTCGAToolbox_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCGAToolbox_2.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCGAToolbox_2.26.1.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData, TCGAWorkflow suggestsMe: TCGAutils dependencyCount: 115 Package: RTN Version: 2.20.0 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5d9208213e07fbfe2f0cd1a864bbcc3a NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_15 git_last_commit: 492e298 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTN_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTN_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTN_2.20.0.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 121 Package: RTNduals Version: 1.20.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d7e23f124ba01dc3a99b006ca76a07ec NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_15 git_last_commit: be908ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTNduals_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTNduals_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTNduals_1.20.0.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 122 Package: RTNsurvival Version: 1.20.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 65d74eb207cb60b1b26bb88d135c4460 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_15 git_last_commit: 86e892c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTNsurvival_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTNsurvival_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTNsurvival_1.20.0.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 126 Package: RTopper Version: 1.42.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 86ee8188a14fc946be89cd3b4760a1d3 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: RELEASE_3_15 git_last_commit: 2c803bf git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RTopper_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTopper_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTopper_1.42.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 16 Package: Rtpca Version: 1.6.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown License: GPL-3 MD5sum: b04f45e6b8da64fc2fa74fa5689d2f17 NeedsCompilation: no Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: RELEASE_3_15 git_last_commit: 8f5d600 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rtpca_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rtpca_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rtpca_1.6.0.tgz vignettes: vignettes/Rtpca/inst/doc/Rtpca.html vignetteTitles: Introduction to Rtpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R dependencyCount: 49 Package: rtracklayer Version: 1.56.1 Depends: R (>= 3.5.0), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO, tools, restfulr (>= 0.0.13) LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE MD5sum: e275748ea08902f8d7cbbe6472b71276 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_15 git_last_commit: 4c6d220 git_last_commit_date: 2022-06-22 Date/Publication: 2022-06-23 source.ver: src/contrib/rtracklayer_1.56.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rtracklayer_1.56.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rtracklayer_1.56.1.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BRGenomics, BSgenome, CAGEfightR, CoverageView, CSSQ, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro, HiCfeat importsMe: AnnotationHubData, annotatr, APAlyzer, ASpediaFI, ATACseqQC, ballgown, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, chromswitch, circRNAprofiler, cliProfiler, CNEr, coMET, compartmap, consensusSeekeR, contiBAIT, conumee, customProDB, dasper, DeepBlueR, derfinder, DEScan2, diffHic, diffloop, diffUTR, DMCFB, DMCHMM, dmrseq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, EpiCompare, epidecodeR, epigraHMM, erma, esATAC, extraChIPs, fcScan, FindIT2, FLAMES, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, gmapR, gmoviz, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, MACPET, MADSEQ, maser, MEDIPS, metagene, metagene2, metaseqR2, methrix, methylKit, motifbreakR, MotifDb, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, NxtIRFcore, ODER, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, ribosomeProfilingQC, rifi, RIPAT, RLSeq, rmspc, RNAmodR, roar, scanMiRApp, scDblFinder, scPipe, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, SigsPack, sitadela, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, spatialLIBD, SingscoreAMLMutations, crispRdesignR, GALLO, geneHapR, ggcoverage, kibior, PlasmaMutationDetector, PlasmaMutationDetector2, utr.annotation, valr suggestsMe: alpine, AnnotationHub, autonomics, BiocFileCache, biovizBase, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, DAMEfinder, eisaR, epimutacions, epistack, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, GenomicRanges, goseq, gwascat, InPAS, interactiveDisplay, megadepth, methylumi, miRBaseConverter, MutationalPatterns, OrganismDbi, Pi, PICS, PING, pipeFrame, plotgardener, pqsfinder, ProteoDisco, R453Plus1Toolbox, RcisTarget, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, signeR, similaRpeak, SynExtend, systemPipeR, TAPseq, TCGAutils, triplex, tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, FDb.FANTOM4.promoters.hg19, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, systemPipeRdata, chipseqDB, gkmSVM, LDheatmap, Rgff, RTIGER, Seurat, Signac, xQTLbiolinks dependencyCount: 44 Package: Rtreemix Version: 1.58.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: 4ee07db083fe0386fb05cba22a0a8380 NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: RELEASE_3_15 git_last_commit: 921e0a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Rtreemix_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rtreemix_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rtreemix_1.58.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R dependencyCount: 74 Package: rTRM Version: 1.34.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: methods, AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: f609db1f7ac6ef9e7191b4b45e657a17 NeedsCompilation: no Title: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: RELEASE_3_15 git_last_commit: addc6b0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rTRM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rTRM_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rTRM_1.34.0.tgz vignettes: vignettes/rTRM/inst/doc/Introduction.html vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/Introduction.R importsMe: rTRMui dependencyCount: 50 Package: rTRMui Version: 1.34.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 Archs: x64 MD5sum: 90ed27f8689b2e6fef66fa323abedd44 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: RELEASE_3_15 git_last_commit: cf193ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rTRMui_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rTRMui_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rTRMui_1.34.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 98 Package: runibic Version: 1.18.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE MD5sum: cf34db953092beca2f1e17c57940f2e5 NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_15 git_last_commit: 492f0ed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/runibic_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/runibic_1.18.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: mosbi dependencyCount: 83 Package: RUVcorr Version: 1.28.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 MD5sum: 3755df6962b7ae3726d148c7161fd6e8 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: RELEASE_3_15 git_last_commit: 5ce53a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RUVcorr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVcorr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVcorr_1.28.0.tgz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 35 Package: RUVnormalize Version: 1.30.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 Archs: x64 MD5sum: ebf1db3d9e8de9f8d71c8d4b9619d24c NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: RELEASE_3_15 git_last_commit: e4c3dd9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RUVnormalize_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVnormalize_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVnormalize_1.30.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 7 Package: RUVSeq Version: 1.30.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 6b6aa5f5a945f2d556e7b98ecfb6e285 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: RELEASE_3_15 git_last_commit: 4964569 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RUVSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVSeq_1.30.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone suggestsMe: DEScan2 dependencyCount: 115 Package: RVS Version: 1.18.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 6060acbbafd65bc44771721f70eab49b NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_15 git_last_commit: e81fa89 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/RVS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RVS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RVS_1.18.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 46 Package: rWikiPathways Version: 1.16.0 Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4807a72c3586590cb71bcfe312eed3fc NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (), Alex Pico [aut] () Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_15 git_last_commit: 757b032 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/rWikiPathways_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rWikiPathways_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rWikiPathways_1.16.0.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: famat, multiSight, TimiRGeN, RVA suggestsMe: TRONCO dependencyCount: 38 Package: S4Vectors Version: 0.34.0 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.37.0) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle License: Artistic-2.0 MD5sum: b03d4bdc69c6b5d7618b0283afbf1dfb NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pagès, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/S4Vectors BugReports: https://github.com/Bioconductor/S4Vectors/issues git_url: https://git.bioconductor.org/packages/S4Vectors git_branch: RELEASE_3_15 git_last_commit: f590de3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/S4Vectors_0.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/S4Vectors_0.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/S4Vectors_0.34.0.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf, vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu, bandle, Biostrings, BiSeq, BRGenomics, BSgenome, bumphunter, Cardinal, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, ClassifyR, cliProfiler, CODEX, CompoundDb, coseq, CSAR, CSSQ, DelayedArray, DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, epihet, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicScores, GenomicTuples, GeomxTools, girafe, groHMM, Gviz, HelloRanges, InTAD, IntEREst, IRanges, LinTInd, LoomExperiment, m6Aboost, MetNet, MotifDb, MSnbase, MuData, NADfinder, NanoStringNCTools, NBAMSeq, OGRE, OTUbase, padma, plethy, PSMatch, Rcwl, RegEnrich, RepViz, RNAmodR, RnBeads, scDataviz, segmentSeq, SeqGate, Spectra, SQLDataFrame, Structstrings, SummarizedBenchmark, TimeSeriesExperiment, topdownr, TreeSummarizedExperiment, TRESS, triplex, updateObject, VariantExperiment, VariantTools, vulcan, XVector, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, SNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, scATAC.Explorer, generegulation, pagoo importsMe: ADImpute, affycoretools, aggregateBioVar, airpart, ALDEx2, AllelicImbalance, alpine, amplican, ANCOMBC, AneuFinder, animalcules, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, appreci8R, ASpediaFI, ASpli, ASURAT, atena, autonomics, BadRegionFinder, ballgown, barcodetrackR, BASiCS, batchelor, BayesSpace, BindingSiteFinder, BiocIO, BiocNeighbors, BiocOncoTK, BiocSet, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, BitSeq, bluster, bnbc, BPRMeth, BrainSABER, branchpointer, breakpointR, BSgenome, bsseq, BumpyMatrix, BUSpaRse, BUSseq, CAGEfightR, CAGEr, casper, CATALYST, cBioPortalData, ccfindR, celaref, celda, CellaRepertorium, CellBarcode, censcyt, Cepo, CeTF, CHETAH, ChIC, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, ChromSCape, chromstaR, chromswitch, chromVAR, cicero, circRNAprofiler, CiteFuse, cleanUpdTSeq, cleaver, CluMSID, clusterExperiment, clustifyr, cn.mops, CNEr, CNVMetrics, CNVPanelizer, CNVRanger, COCOA, CoGAPS, Cogito, comapr, coMET, compEpiTools, consensusDE, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoreGx, CoverageView, crisprBase, CRISPRseek, CrispRVariants, csaw, CTDquerier, cummeRbund, customProDB, cydar, cytoKernel, cytomapper, DAMEfinder, dasper, debrowser, DECIPHER, decompTumor2Sig, deconvR, DEFormats, DegNorm, DEGreport, DelayedMatrixStats, derfinder, derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DiffBind, diffcyt, diffHic, diffloop, diffUTR, Dino, DiscoRhythm, dittoSeq, DMRcate, dmrseq, doseR, DRIMSeq, DropletUtils, drugTargetInteractions, dStruct, easyRNASeq, eegc, eisaR, ELMER, enhancerHomologSearch, EnrichmentBrowser, enrichTF, ensembldb, ensemblVEP, EpiCompare, epigraHMM, epimutacions, epistack, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, ExperimentHub, ExperimentSubset, ExploreModelMatrix, extraChIPs, FastqCleaner, fastseg, FilterFFPE, FindIT2, fishpond, FLAMES, flowCore, flowWorkspace, FRASER, GA4GHshiny, gcapc, GDSArray, genbankr, GeneRegionScan, GENESIS, GeneTonic, genomation, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicSuperSignature, ggbio, Glimma, gmapR, gmoviz, GOpro, GOTHiC, GRaNIE, GRmetrics, GSEABenchmarkeR, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, hermes, HiCBricks, HiCcompare, HiLDA, hipathia, hmdbQuery, HTSeqGenie, HumanTranscriptomeCompendium, icetea, ideal, ILoReg, IMAS, imcRtools, INSPEcT, InteractionSet, InteractiveComplexHeatmap, InterMineR, iSEE, iSEEu, isomiRs, IVAS, ivygapSE, karyoploteR, kebabs, lionessR, lipidr, lisaClust, loci2path, LOLA, MACPET, MACSr, MADSEQ, MAI, marr, MAST, MatrixQCvis, mbkmeans, mCSEA, MEAL, meshr, MesKit, metabCombiner, MetaboAnnotation, metaseqR2, MetCirc, methInheritSim, MethReg, methylCC, methylInheritance, methylKit, methylPipe, methylSig, methylumi, mia, miaSim, miaViz, microbiomeMarker, midasHLA, miloR, mimager, minfi, MinimumDistance, MIRA, MiRaGE, missMethyl, missRows, mitoClone2, MMAPPR2, MMDiff2, moanin, Modstrings, monaLisa, mosaics, MOSim, Motif2Site, motifbreakR, motifmatchr, mpra, msa, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsCoreUtils, msgbsR, MSPrep, MultiAssayExperiment, MultiDataSet, mumosa, muscat, musicatk, MutationalPatterns, mygene, myvariant, NanoMethViz, ncRNAtools, nearBynding, nucleoSim, nucleR, nullranges, NxtIRFcore, ODER, oligoClasses, omicsViewer, ontoProc, openPrimeR, ORFik, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, PAIRADISE, panelcn.mops, PAST, pcaExplorer, PDATK, pdInfoBuilder, periodicDNA, PharmacoGx, phemd, PhIPData, PhosR, PING, pipeComp, plyranges, pmp, pogos, polyester, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, procoil, proDA, profileplyr, ProteoDisco, pulsedSilac, PureCN, PWMEnrich, qcmetrics, QFeatures, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, RcisTarget, RcwlPipelines, recount, recount3, recountmethylation, recoup, regioneR, regionReport, regsplice, regutools, REMP, Repitools, ResidualMatrix, restfulSE, rexposome, rfaRm, RGMQL, rhdf5client, RiboDiPA, RiboProfiling, ribor, ribosomeProfilingQC, rifi, RJMCMCNucleosomes, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, rprimer, Rqc, Rsamtools, rScudo, RTCGAToolbox, RTN, rtracklayer, SC3, ScaledMatrix, scanMiR, scanMiRApp, SCArray, scater, scClassify, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, SCOPE, scp, scPipe, scran, scruff, scTensor, scTGIF, scTreeViz, scuttle, sechm, segmenter, SeqArray, seqCAT, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead, SingleCellExperiment, singleCellTK, SingleR, singscore, sitadela, skewr, slingshot, SMITE, SNPhood, soGGi, SomaticSignatures, Spaniel, spatialDE, SpatialExperiment, spatialHeatmap, spatzie, spicyR, spiky, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff, STAN, standR, strandCheckR, struct, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, SynExtend, systemPipeR, TAPseq, TarSeqQC, TBSignatureProfiler, TCGAbiolinks, TCGAutils, terraTCGAdata, TFBSTools, TFHAZ, tidySingleCellExperiment, tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq, TrajectoryUtils, transcriptR, TransView, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TVTB, twoddpcr, txcutr, tximeta, Ularcirc, UMI4Cats, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, VaSP, VCFArray, velociraptor, VplotR, wavClusteR, weitrix, wiggleplotr, xcms, xcore, XNAString, XVector, yamss, zellkonverter, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DropletTestFiles, FlowSorted.Blood.EPIC, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, imcdatasets, leeBamViews, MetaGxPancreas, MethylSeqData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, tuberculosis, GeoMxWorkflows, ActiveDriverWGS, crispRdesignR, digitalDLSorteR, DR.SC, driveR, genBaRcode, geno2proteo, ggcoverage, hoardeR, IFAA, imcExperiment, LoopRig, microbial, NIPTeR, oncoPredict, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, restfulr, rsolr, SC.MEB, SCRIP, Signac, SNPassoc, toxpiR suggestsMe: AlpsNMR, BiocGenerics, BioPlex, conclus, dearseq, epivizrChart, globalSeq, GWASTools, GWENA, hca, maftools, martini, MicrobiotaProcess, MungeSumstats, RTCGA, SPOTlight, TFEA.ChIP, TFutils, tidybulk, traviz, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, cancerTiming, GeoTcgaData, gkmSVM, polyRAD, Rgff, rliger, Seurat, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 6 Package: safe Version: 3.36.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: b61448bf95959d7ba77c96e8677dca8f NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/safe git_branch: RELEASE_3_15 git_last_commit: 53a1d06 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/safe_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/safe_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/safe_3.36.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R dependsOnMe: PCGSE importsMe: EGSEA, EnrichmentBrowser dependencyCount: 46 Package: sagenhaft Version: 1.66.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: c494e2d65cf709ae03f0618f6c9cefe7 NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: RELEASE_3_15 git_last_commit: 8bd6d5f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sagenhaft_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sagenhaft_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sagenhaft_1.66.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAIGEgds Version: 1.10.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.31.8), Rcpp Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics, SNPRelate, ggmanh License: GPL-3 MD5sum: d450784ba76661c531c3311daf808232 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the original SAIGE R package (v0.29.4.4 for single variant tests, Zhou et al. 2018). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original SAIGE R package. biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation Author: Xiuwen Zheng [aut, cre] (), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: RELEASE_3_15 git_last_commit: 9c55c6f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SAIGEgds_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SAIGEgds_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SAIGEgds_1.10.0.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 26 Package: sampleClassifier Version: 1.20.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: ebd86ac50db738c615b95ffe51252353 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: RELEASE_3_15 git_last_commit: eb3bc26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sampleClassifier_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sampleClassifier_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sampleClassifier_1.20.0.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R dependencyCount: 95 Package: SamSPECTRAL Version: 1.50.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) Archs: x64 MD5sum: c8fb7e4cd47797a06849e9a19128175c NeedsCompilation: yes Title: Identifies cell population in flow cytometry data Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: RELEASE_3_15 git_last_commit: 2560496 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SamSPECTRAL_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SamSPECTRAL_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SamSPECTRAL_1.50.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.6.1 Depends: R (>= 4.0.0), stringr, ape, Biostrings, DECIPHER, parallel, reshape2, phangorn, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle, logger Suggests: testthat (>= 2.1.0) License: GPL-2 MD5sum: bf853f7e61708f103ac9dbe97aa3e325 NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: RELEASE_3_15 git_last_commit: d939fcb git_last_commit_date: 2022-05-04 Date/Publication: 2022-05-15 source.ver: src/contrib/sangeranalyseR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/sangeranalyseR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/sangeranalyseR_1.6.1.tgz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 134 Package: sangerseqR Version: 1.32.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 94dbdda2c98d97be83fc68ee0c779ea1 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: RELEASE_3_15 git_last_commit: 4f81464 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sangerseqR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sangerseqR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sangerseqR_1.32.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R dependsOnMe: sangeranalyseR suggestsMe: CrispRVariants, bold dependencyCount: 48 Package: SANTA Version: 2.32.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, DLBCL, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: f703928e009656493667aa2654cdccc7 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: RELEASE_3_15 git_last_commit: b7d20f5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SANTA_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SANTA_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SANTA_2.32.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 12 Package: sarks Version: 1.8.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE MD5sum: 32c37b2e93430a0bc140a6aefdd91d45 NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] () Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: RELEASE_3_15 git_last_commit: e8df086 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sarks_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sarks_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sarks_1.8.0.tgz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 21 Package: satuRn Version: 1.4.2 Depends: R (>= 4.1) Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply, ggplot2, boot, Matrix, stats, methods, graphics Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub, ensembldb, edgeR, DEXSeq, stageR, DelayedArray License: Artistic-2.0 Archs: x64 MD5sum: 49d34c020a817741490426a08f580843 NeedsCompilation: no Title: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications Description: satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. biocViews: Regression, ExperimentalDesign, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb] Maintainer: Jeroen Gilis URL: https://github.com/statOmics/satuRn VignetteBuilder: knitr BugReports: https://github.com/statOmics/satuRn/issues git_url: https://git.bioconductor.org/packages/satuRn git_branch: RELEASE_3_15 git_last_commit: e97202a git_last_commit_date: 2022-08-05 Date/Publication: 2022-08-07 source.ver: src/contrib/satuRn_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/satuRn_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/satuRn_1.4.2.tgz vignettes: vignettes/satuRn/inst/doc/Vignette.html vignetteTitles: satuRn - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/satuRn/inst/doc/Vignette.R dependencyCount: 66 Package: savR Version: 1.34.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 857cd04071f4c89d64b4eef597faa505 NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues git_url: https://git.bioconductor.org/packages/savR git_branch: RELEASE_3_15 git_last_commit: 27b2a01 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/savR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/savR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/savR_1.34.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R dependencyCount: 44 Package: SBGNview Version: 1.10.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 MD5sum: 74cbc1c7e46dabc188b44dda6d7dc535 NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: RELEASE_3_15 git_last_commit: b9f5435 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SBGNview_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SBGNview_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SBGNview_1.10.0.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 84 Package: SBMLR Version: 1.92.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: fb6664cd09ffc0563e2a7153cf2c1225 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: RELEASE_3_15 git_last_commit: 4af9828 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SBMLR_1.92.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SBMLR_1.92.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SBMLR_1.92.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.24.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater License: GPL-3 MD5sum: 4378000aaf8bfcd1d6fc9330663154b0 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: RELEASE_3_15 git_last_commit: 6b37d29 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SC3_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SC3_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SC3_1.24.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R importsMe: FEAST suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs dependencyCount: 101 Package: Scale4C Version: 1.18.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 07fccc637dd0fdb2ea1856a8710ff9bd NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: RELEASE_3_15 git_last_commit: 3532716 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Scale4C_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Scale4C_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Scale4C_1.18.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 26 Package: ScaledMatrix Version: 1.4.1 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 Archs: x64 MD5sum: 6418d59e8761f118adb603ccb086ceb8 NeedsCompilation: no Title: Creating a DelayedMatrix of Scaled and Centered Values Description: Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. biocViews: Software, DataRepresentation Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ScaledMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ScaledMatrix/issues git_url: https://git.bioconductor.org/packages/ScaledMatrix git_branch: RELEASE_3_15 git_last_commit: 15e2efc git_last_commit_date: 2022-09-08 Date/Publication: 2022-09-11 source.ver: src/contrib/ScaledMatrix_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ScaledMatrix_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ScaledMatrix_1.4.1.tgz vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html vignetteTitles: Using the ScaledMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R importsMe: batchelor, BiocSingular, mumosa, scPCA suggestsMe: scran dependencyCount: 15 Package: scAlign Version: 1.9.0 Depends: R (>= 3.6), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 19afbe5209347df6e169592d22c79bc2 NeedsCompilation: no Title: An alignment and integration method for single cell genomics Description: An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) for more details. biocViews: SingleCell, Transcriptomics, DimensionReduction, NeuralNetwork Author: Nelson Johansen [aut, cre], Gerald Quon [aut] Maintainer: Nelson Johansen URL: https://github.com/quon-titative-biology/scAlign SystemRequirements: python (< 3.7), tensorflow VignetteBuilder: knitr BugReports: https://github.com/quon-titative-biology/scAlign/issues git_url: https://git.bioconductor.org/packages/scAlign git_branch: master git_last_commit: 60eccb4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/scAlign_1.9.0.tar.gz vignettes: vignettes/scAlign/inst/doc/scAlign.pdf vignetteTitles: alignment_tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAlign/inst/doc/scAlign.R dependencyCount: 170 Package: SCAN.UPC Version: 2.38.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 4ade1c299159a07d3399e7c43fa58376 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: RELEASE_3_15 git_last_commit: 23f3e83 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCAN.UPC_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCAN.UPC_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCAN.UPC_2.38.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 112 Package: scanMiR Version: 1.2.0 Depends: R (>= 4.0) Imports: Biostrings, GenomicRanges, IRanges, data.table, BiocParallel, methods, GenomeInfoDb, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, ggseqlogo, cowplot Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: a3e782a40470e7da06ae590335f424de NeedsCompilation: no Title: scanMiR Description: A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment). biocViews: miRNA, SequenceMatching, Alignment Author: Pierre-Luc Germain [aut] (), Michael Soutschek [aut], Fridolin Gross [cre, aut] Maintainer: Fridolin Gross VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: RELEASE_3_15 git_last_commit: 3fef2db git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scanMiR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scanMiR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scanMiR_1.2.0.tgz vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html, vignettes/scanMiR/inst/doc/scanning.html vignetteTitles: 2_Kdmodels, 1_scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R importsMe: scanMiRApp, scanMiRData dependencyCount: 63 Package: scanMiRApp Version: 1.2.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiR, scanMiRData, shiny, shinycssloaders, shinydashboard, shinyjqui, stats, utils, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 MD5sum: 110bf1772d2a80c2a5578a2c05d4828d NeedsCompilation: no Title: scanMiR shiny application Description: A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs. biocViews: miRNA, SequenceMatching, GUI Author: Pierre-Luc Germain [cre, aut] (), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiRApp git_branch: RELEASE_3_15 git_last_commit: dc17bc4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scanMiRApp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scanMiRApp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scanMiRApp_1.2.0.tgz vignettes: vignettes/scanMiRApp/inst/doc/IndexedFST.html, vignettes/scanMiRApp/inst/doc/scanMiRApp.html vignetteTitles: IndexedFst, scanMiRApp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scanMiRApp/inst/doc/IndexedFST.R, vignettes/scanMiRApp/inst/doc/scanMiRApp.R dependencyCount: 151 Package: scAnnotatR Version: 1.2.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE MD5sum: 509e36dac7c02ce93963cbb1b16a1e62 NeedsCompilation: no Title: Pretrained learning models for cell type prediction on single cell RNA-sequencing data Description: The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs. biocViews: SingleCell, Transcriptomics, GeneExpression, SupportVectorMachine, Classification, Software Author: Vy Nguyen [aut] (), Johannes Griss [cre] () Maintainer: Johannes Griss URL: https://github.com/grisslab/scAnnotatR VignetteBuilder: knitr BugReports: https://github.com/grisslab/scAnnotatR/issues/new git_url: https://git.bioconductor.org/packages/scAnnotatR git_branch: RELEASE_3_15 git_last_commit: e9d43a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scAnnotatR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scAnnotatR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scAnnotatR_1.2.0.tgz vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html, vignettes/scAnnotatR/inst/doc/training-basic-model.html, vignettes/scAnnotatR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R, vignettes/scAnnotatR/inst/doc/training-basic-model.R, vignettes/scAnnotatR/inst/doc/training-child-model.R suggestsMe: scAnnotatR.models dependencyCount: 201 Package: SCANVIS Version: 1.10.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 83f62567b3f91365849b1f6367abb8f1 NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: RELEASE_3_15 git_last_commit: d559a6b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCANVIS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCANVIS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCANVIS_1.10.0.tgz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 46 Package: SCArray Version: 1.4.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.27.4), methods, DelayedArray (>= 0.16.0) Imports: BiocGenerics, S4Vectors, IRanges, utils, SummarizedExperiment, SingleCellExperiment, DelayedMatrixStats Suggests: Matrix, scater, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 MD5sum: c20c53af516274f315e2e780a72a2c0b NeedsCompilation: yes Title: Large-scale single-cell RNA-seq data manipulation with GDS files Description: Provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language. biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] () Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: RELEASE_3_15 git_last_commit: d6dbbc3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCArray_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCArray_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCArray_1.4.0.tgz vignettes: vignettes/SCArray/inst/doc/Overview.html, vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: Overview, Single-cell RNA-seq data manipulation using GDS files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray/inst/doc/SCArray.R dependencyCount: 30 Package: SCATE Version: 1.6.0 Depends: parallel, preprocessCore, splines, splines2, xgboost, SCATEData, Rtsne, mclust Imports: utils, stats, GenomicAlignments, GenomicRanges Suggests: rmarkdown, ggplot2, knitr License: MIT + file LICENSE MD5sum: 27d44cfc0e936094b9556a0f090de2f0 NeedsCompilation: no Title: SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement Description: SCATE is a software tool for extracting and enhancing the sparse and discrete Single-cell ATAC-seq Signal. Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. SCATE was developed to adaptively integrate information from co-activated CREs, similar cells, and publicly available regulome data and substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample. biocViews: ExperimentHub, ExperimentData, Genome, SequencingData, SingleCellData, SNPData Author: Zhicheng Ji [aut], Weiqiang Zhou [aut], Wenpin Hou [cre, aut] (), Hongkai Ji [aut] Maintainer: Wenpin Hou VignetteBuilder: knitr BugReports: https://github.com/Winnie09/SCATE/issues git_url: https://git.bioconductor.org/packages/SCATE git_branch: RELEASE_3_15 git_last_commit: 49cdb67 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCATE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCATE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCATE_1.6.0.tgz vignettes: vignettes/SCATE/inst/doc/SCATE.html vignetteTitles: 1. SCATE package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCATE/inst/doc/SCATE.R dependencyCount: 117 Package: scater Version: 1.24.0 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, grid, gridExtra, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, ggrepel Suggests: BiocStyle, biomaRt, cowplot, knitr, scRNAseq, robustbase, rmarkdown, uwot, testthat, pheatmap, snifter, Biobase License: GPL-3 Archs: x64 MD5sum: 28234d8179d3f166baeb00d43812a7b2 NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M. Ernst [ctb], Alan O'Callaghan [ctb, cre] Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_15 git_last_commit: 013f093 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scater_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scater_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scater_1.24.0.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: netSmooth, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS, ChromSCape, conclus, distinct, FLAMES, IRISFGM, mia, miaViz, muscat, peco, pipeComp, scDblFinder, scPipe, scTreeViz, singleCellTK, Spaniel, spatialHeatmap, splatter, tricycle, VAExprs, spatialLIBD, PRECAST, SC.MEB suggestsMe: APL, batchelor, bluster, CellaRepertorium, CellTrails, Cepo, CiteFuse, corral, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, InteractiveComplexHeatmap, iSEE, iSEEu, M3Drop, MAST, mbkmeans, miloR, miQC, monocle, MuData, mumosa, Nebulosa, netDx, SC3, SCArray, scds, schex, scHOT, scMerge, scone, scp, scran, scRepertoire, SingleR, slalom, SPOTlight, standR, SummarizedBenchmark, tidySingleCellExperiment, traviz, UCell, velociraptor, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, SingleRBook, bcTSNE dependencyCount: 83 Package: scatterHatch Version: 1.2.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: ea0cd4ec1a05154256e7481879172394 NeedsCompilation: no Title: Creates hatched patterns for scatterplots Description: The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness. biocViews: Visualization, SingleCell, CellBiology, Software, Spatial Author: Atul Deshpande [aut, cre] () Maintainer: Atul Deshpande URL: https://github.com/FertigLab/scatterHatch VignetteBuilder: knitr BugReports: https://github.com/FertigLab/scatterHatch/issues git_url: https://git.bioconductor.org/packages/scatterHatch git_branch: RELEASE_3_15 git_last_commit: df8c6ca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scatterHatch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scatterHatch_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scatterHatch_1.2.0.tgz vignettes: vignettes/scatterHatch/inst/doc/vignette.html vignetteTitles: Creating a Scatterplot with Texture hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scatterHatch/inst/doc/vignette.R dependencyCount: 43 Package: scBFA Version: 1.10.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: 18f514a9642eb344cc61e4db145cf796 NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: RELEASE_3_15 git_last_commit: 702e3b9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scBFA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scBFA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scBFA_1.10.0.tgz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 194 Package: SCBN Version: 1.14.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 1b267e1afcec635c8486f896571bbbbe NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: RELEASE_3_15 git_last_commit: 33f78e4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCBN_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCBN_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCBN_1.14.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R importsMe: TEKRABber dependencyCount: 1 Package: scCB2 Version: 1.6.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 90898e9c8c38411a7e6c8021e7387526 NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues git_url: https://git.bioconductor.org/packages/scCB2 git_branch: RELEASE_3_15 git_last_commit: b2d87b4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scCB2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scCB2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scCB2_1.6.0.tgz vignettes: vignettes/scCB2/inst/doc/scCB2.html vignetteTitles: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scCB2/inst/doc/scCB2.R dependencyCount: 185 Package: scClassify Version: 1.8.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 3568279e85abec23b256202f0e0386fd NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: RELEASE_3_15 git_last_commit: 2d852ba git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scClassify_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scClassify_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scClassify_1.8.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 135 Package: sccomp Version: 1.0.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.18.1), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, testthat (>= 3.0.0), markdown, ggplot2, knitr, tidyseurat, tidySingleCellExperiment Enhances: furrr, extraDistr License: GPL-3 MD5sum: 2cc466053ee2b070f5b56ff7376348ce NeedsCompilation: yes Title: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data Description: A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/sccomp SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: RELEASE_3_15 git_last_commit: dcc1b26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sccomp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sccomp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sccomp_1.0.0.tgz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: Overview of the sccomp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 106 Package: scDataviz Version: 1.6.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown License: GPL-3 MD5sum: e56ab44112e33d52dd2ca2591273f0b6 NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: RELEASE_3_15 git_last_commit: d048cf8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scDataviz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDataviz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDataviz_1.6.0.tgz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 169 Package: scDblFinder Version: 1.10.0 Depends: R (>= 4.0) Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, SingleCellExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans License: GPL-3 + file LICENSE Archs: x64 MD5sum: 2533ee32f3b147723fba53b8ecbc94a8 NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq Author: Pierre-Luc Germain [cre, aut] (), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_15 git_last_commit: 03512ca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scDblFinder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDblFinder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDblFinder_1.10.0.tgz vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/findDoubletClusters.html, vignettes/scDblFinder/inst/doc/introduction.html, vignettes/scDblFinder/inst/doc/recoverDoublets.html, vignettes/scDblFinder/inst/doc/scATAC.html, vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R, vignettes/scDblFinder/inst/doc/findDoubletClusters.R, vignettes/scDblFinder/inst/doc/introduction.R, vignettes/scDblFinder/inst/doc/recoverDoublets.R, vignettes/scDblFinder/inst/doc/scATAC.R, vignettes/scDblFinder/inst/doc/scDblFinder.R dependsOnMe: OSCA.advanced importsMe: singleCellTK dependencyCount: 109 Package: scDD Version: 1.20.0 Depends: R (>= 3.5.0) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 138c051792fff51e66ee88b5f64270b9 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] () Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: RELEASE_3_15 git_last_commit: caeb421 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scDD_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDD_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDD_1.20.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 124 Package: scde Version: 2.24.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 MD5sum: 2db0219b4d57810222bf38e07e93ce2a NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut] Maintainer: Jean Fan URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: RELEASE_3_15 git_last_commit: 59a8d5a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scde_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scde_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scde_2.24.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 47 Package: scds Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown License: MIT + file LICENSE MD5sum: 02718680669609c19361bad7a769bd8c NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: RELEASE_3_15 git_last_commit: 406e313 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scds_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scds_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scds_1.12.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 48 Package: SCFA Version: 1.6.0 Depends: R (>= 4.0) Imports: matrixStats, keras, tensorflow, BiocParallel, igraph, Matrix, cluster, clusterCrit, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown License: LGPL Archs: x64 MD5sum: e5f9e8ed9d98a203e830c2f4821d6fc4 NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: RELEASE_3_15 git_last_commit: daa94d2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCFA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCFA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCFA_1.6.0.tgz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 65 Package: scFeatureFilter Version: 1.16.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 52d62af122215e93b362f842da516f5e NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: RELEASE_3_15 git_last_commit: 4948ee2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scFeatureFilter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scFeatureFilter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scFeatureFilter_1.16.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 39 Package: scGPS Version: 1.10.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq2, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 MD5sum: fb9c001b85d2a7145ec1a996ea2ddb10 NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: RELEASE_3_15 git_last_commit: 30d3fb6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scGPS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scGPS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scGPS_1.10.0.tgz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 139 Package: schex Version: 1.10.0 Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 (>= 3.2.1), shiny Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, scales, grid, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran License: GPL-3 MD5sum: bab0f5aff3038bd6c5ca68c21a3eef94 NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment and SeuratObject. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization Author: Saskia Freytag Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: RELEASE_3_15 git_last_commit: 6945ee6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/schex_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/schex_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/schex_1.10.0.tgz vignettes: vignettes/schex/inst/doc/multi_modal_schex.html, vignettes/schex/inst/doc/picking_the_right_resolution.html, vignettes/schex/inst/doc/Seurat_schex.html, vignettes/schex/inst/doc/shiny_schex.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: multi_modal_schex, picking_the_right_resolution, Seurat_schex, shiny_schhex, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/multi_modal_schex.R, vignettes/schex/inst/doc/picking_the_right_resolution.R, vignettes/schex/inst/doc/Seurat_schex.R, vignettes/schex/inst/doc/shiny_schex.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF suggestsMe: fcoex dependencyCount: 177 Package: scHOT Version: 1.8.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 Archs: x64 MD5sum: a7959213921c69224ea8ade26e81f932 NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: RELEASE_3_15 git_last_commit: 2267aad git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scHOT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scHOT_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scHOT_1.8.0.tgz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 74 Package: scMAGeCK Version: 1.8.0 Imports: Seurat, ggplot2, stats, utils Suggests: knitr, rmarkdown License: BSD_2_clause MD5sum: 684c4bfab987a37fcca9064808566b6d NeedsCompilation: yes Title: Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data Description: scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq) biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics, GeneExpression, Regression Author: Wei Li, Xiaolong Cheng, Lin Yang Maintainer: Xiaolong Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scMAGeCK git_branch: RELEASE_3_15 git_last_commit: e3283d7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scMAGeCK_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scMAGeCK_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scMAGeCK_1.8.0.tgz vignettes: vignettes/scMAGeCK/inst/doc/scMAGeCK.html vignetteTitles: scMAGeCK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMAGeCK/inst/doc/scMAGeCK.R dependencyCount: 146 Package: scmap Version: 1.18.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b3a4d45e0e7b391aa5a415b918b5f0a2 NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: RELEASE_3_15 git_last_commit: c823f6c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scmap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scmap_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scmap_1.18.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 69 Package: scMerge Version: 1.12.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel, pdist, proxy, ruv, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, scater, testthat, badger License: GPL-3 MD5sum: f659f5512c412e082d90696f39d06c9f NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell RNA-seq (scRNA-Seq) data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple scRNA-Seq data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of scRNA-Seq data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: RELEASE_3_15 git_last_commit: 20038d0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scMerge_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scMerge_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scMerge_1.12.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html vignetteTitles: scMerge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 137 Package: scmeth Version: 1.16.0 Depends: R (>= 3.5.0) Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics, ggplot2, ggthemes License: GPL-2 Archs: x64 MD5sum: 513396f0490e593af64f7acc52682de5 NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: RELEASE_3_15 git_last_commit: 4ea6e62 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scmeth_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scmeth_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scmeth_1.16.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 165 Package: SCnorm Version: 1.18.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 46fa56922a1185a3836e1c67d11dfae1 NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_15 git_last_commit: adca56c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCnorm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCnorm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCnorm_1.18.0.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 72 Package: scone Version: 1.20.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, BatchJobs, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 MD5sum: bcd5f719852c797302f1a76cb4e897ea NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: RELEASE_3_15 git_last_commit: eb71c4f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scone_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scone_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scone_1.20.0.tgz vignettes: vignettes/scone/inst/doc/PsiNorm.html, vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: PsiNorm normalization, Introduction to SCONE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/PsiNorm.R, vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 149 Package: Sconify Version: 1.16.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: f0d136a0bc0ddf90ce00f55b01f0aaa1 NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_15 git_last_commit: fa58d50 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Sconify_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Sconify_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Sconify_1.16.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 66 Package: SCOPE Version: 1.8.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: 492f0f965f20879c0325c0e5157b9203 NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: RELEASE_3_15 git_last_commit: 267eed4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SCOPE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCOPE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCOPE_1.8.0.tgz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 99 Package: scoreInvHap Version: 1.18.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: d11817959067eb17798a524df6ca5326 NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_15 git_last_commit: 1725033 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scoreInvHap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scoreInvHap_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scoreInvHap_1.18.0.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 102 Package: scp Version: 1.6.0 Depends: R (>= 4.0), QFeatures (>= 1.3.5) Imports: methods, stats, utils, SingleCellExperiment, SummarizedExperiment, MultiAssayExperiment, MsCoreUtils, matrixStats, S4Vectors, dplyr, magrittr, rlang Suggests: testthat, knitr, BiocStyle, rmarkdown, patchwork, ggplot2, impute, scater, sva, preprocessCore, vsn, uwot License: Artistic-2.0 MD5sum: e039d479e8e709eb6df9fb18a7d971a1 NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics (SCP) data. The package is an extension to the 'QFeatures' package designed for SCP applications. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (), Laurent Gatto [aut] () Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: RELEASE_3_15 git_last_commit: 7fc4fa0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scp_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scp_1.6.0.tgz vignettes: vignettes/scp/inst/doc/advanced.html, vignettes/scp/inst/doc/QFeatures_nutshell.html, vignettes/scp/inst/doc/read_scp.html, vignettes/scp/inst/doc/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/advanced.R, vignettes/scp/inst/doc/QFeatures_nutshell.R, vignettes/scp/inst/doc/read_scp.R, vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 88 Package: scPCA Version: 1.10.0 Depends: R (>= 4.0.0) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: 09e3740d661719872e8b38f1d4385fbc NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (), Nima Hejazi [aut] (), Sandrine Dudoit [ctb, ths] () Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: RELEASE_3_15 git_last_commit: d1380da git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scPCA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scPCA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scPCA_1.10.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependsOnMe: OSCA.advanced, OSCA.workflows dependencyCount: 67 Package: scPipe Version: 1.18.0 Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3), reshape, BiocGenerics, robustbase, scales, utils, stats, S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, stringr, rtracklayer, hash, dplyr, GenomicRanges, magrittr, glue (>= 1.3.0), rlang, scater (>= 1.11.0) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat Suggests: Rsubread, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: fe96630700191fe0217c32a5a20b1533 NeedsCompilation: yes Title: pipeline for single cell RNA-seq data analysis Description: A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation Author: Luyi Tian Maintainer: Luyi Tian URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: RELEASE_3_15 git_last_commit: f18ea8d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scPipe_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scPipe_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scPipe_1.18.0.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 157 Package: scran Version: 1.24.1 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, DelayedArray, DelayedMatrixStats, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, monocle, Biobase, pheatmap, scater License: GPL-3 MD5sum: e46c2f772c4892ab9d8b1c65e959dc16 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_15 git_last_commit: 1a83eb7 git_last_commit_date: 2022-09-08 Date/Publication: 2022-09-11 source.ver: src/contrib/scran_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/scran_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/scran_1.24.1.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: BASiCS, BayesSpace, BioTIP, celda, ChromSCape, CiteFuse, conclus, Dino, FLAMES, IRISFGM, msImpute, mumosa, pipeComp, scDblFinder, scDD, scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, spatialHeatmap, mixhvg, SC.MEB suggestsMe: APL, batchelor, bluster, CellTrails, clusterExperiment, destiny, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, glmGamPoi, iSEEu, miloR, Nebulosa, nnSVG, PCAtools, schex, scone, scuttle, SingleR, splatter, SPOTlight, tidySingleCellExperiment, transformGamPoi, TSCAN, velociraptor, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, SingleRBook dependencyCount: 59 Package: scReClassify Version: 1.2.0 Depends: R (>= 4.1) Imports: randomForest, e1071, stats, SummarizedExperiment, SingleCellExperiment, methods Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 4959de4f77e5dd58a87630cd0fdcbd62 NeedsCompilation: no Title: scReClassify: post hoc cell type classification of single-cell RNA-seq data Description: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier. biocViews: Software, Transcriptomics, SingleCell, Classification, SupportVectorMachine Author: Pengyi Yang [aut] (), Taiyun Kim [aut, cre] () Maintainer: Taiyun Kim URL: https://github.com/SydneyBioX/scReClassify, http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scReClassify/issues git_url: https://git.bioconductor.org/packages/scReClassify git_branch: RELEASE_3_15 git_last_commit: c10e9be git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scReClassify_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scReClassify_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scReClassify_1.2.0.tgz vignettes: vignettes/scReClassify/inst/doc/scReClassify.html vignetteTitles: An introduction to scReClassify package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R dependencyCount: 31 Package: scRecover Version: 1.12.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), Rmagic (>= 1.3.0), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL Archs: x64 MD5sum: c7515853d402844a104879e6972160e1 NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: RELEASE_3_15 git_last_commit: 572d26d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scRecover_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scRecover_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scRecover_1.12.0.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 46 Package: scRepertoire Version: 1.6.0 Depends: ggplot2, R (>= 4.0) Imports: stringdist, dplyr, reshape2, ggalluvial, stringr, vegan, powerTCR, SummarizedExperiment, plyr, parallel, doParallel, methods, utils, rlang, igraph, SeuratObject Suggests: knitr, rmarkdown, BiocStyle, scater, circlize, scales, Seurat License: Apache License 2.0 MD5sum: 732610dcc14d99ed5990e7aaaba0e37f NeedsCompilation: no Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: RELEASE_3_15 git_last_commit: 7ba1676 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scRepertoire_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scRepertoire_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scRepertoire_1.6.0.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R dependencyCount: 95 Package: scruff Version: 1.14.0 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 717f114e6f59e7ef3c08908a9ee0ef77 NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: RELEASE_3_15 git_last_commit: 9299a23 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scruff_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scruff_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scruff_1.14.0.tgz vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 163 Package: scry Version: 1.8.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: markdown, BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: d49ceda89bd689d53a7b4f0698abbfb0 NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: RELEASE_3_15 git_last_commit: 4ef2c46 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scry_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scry_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scry_1.8.0.tgz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R dependencyCount: 47 Package: scShapes Version: 1.2.0 Depends: R (>= 4.1) Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS, emdbook, magrittr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: fcf44cfc6d53d24f13414f9ff2d23da8 NeedsCompilation: yes Title: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data Description: We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic. biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression Author: Malindrie Dharmaratne [cre, aut] () Maintainer: Malindrie Dharmaratne URL: https://github.com/Malindrie/scShapes VignetteBuilder: knitr BugReports: https://github.com/Malindrie/scShapes/issues git_url: https://git.bioconductor.org/packages/scShapes git_branch: RELEASE_3_15 git_last_commit: 17d9616 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scShapes_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scShapes_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scShapes_1.2.0.tgz vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html vignetteTitles: The vignette for running scShapes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R dependencyCount: 33 Package: scTensor Version: 2.6.0 Depends: R (>= 4.1.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 MD5sum: 33f949f728d7571e87939208113a1729 NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: RELEASE_3_15 git_last_commit: 2b4f332 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scTensor_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTensor_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTensor_2.6.0.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 280 Package: scTGIF Version: 1.10.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: e87cfd405aa193128f6a7a51a45d0a9a NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: RELEASE_3_15 git_last_commit: 5c8883f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scTGIF_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTGIF_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTGIF_1.10.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 212 Package: scTHI Version: 1.8.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown License: GPL-2 MD5sum: 61270f0628b7a2f5140424f3f6680b51 NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: RELEASE_3_15 git_last_commit: 140a77a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scTHI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTHI_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTHI_1.8.0.tgz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 16 Package: scTreeViz Version: 1.2.0 Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph, clustree, scran, sys, epivizrData, epivizrServer, ggraph, scater, Seurat, SingleCellExperiment, ggplot2, stats, utils Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s, metagenomeSeq, epivizrStandalone, GenomeInfoDb License: Artistic-2.0 MD5sum: 54c55eeffb5042b302ecd54d6772137f NeedsCompilation: no Title: R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations Description: scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters. biocViews: Visualization, Infrastructure, GUI, SingleCell Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut] Maintainer: Jayaram Kancherla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: RELEASE_3_15 git_last_commit: bf5ce8a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/scTreeViz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTreeViz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTreeViz_1.2.0.tgz vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html vignetteTitles: Explore Data using scTreeViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R dependencyCount: 240 Package: scuttle Version: 1.6.3 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, scran License: GPL-3 MD5sum: 486b257af07121280897b193a6d4d075 NeedsCompilation: yes Title: Single-Cell RNA-Seq Analysis Utilities Description: Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: RELEASE_3_15 git_last_commit: df23680 git_last_commit_date: 2022-08-23 Date/Publication: 2022-08-23 source.ver: src/contrib/scuttle_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/scuttle_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/scuttle_1.6.3.tgz vignettes: vignettes/scuttle/inst/doc/misc.html, vignettes/scuttle/inst/doc/norm.html, vignettes/scuttle/inst/doc/qc.html vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/misc.R, vignettes/scuttle/inst/doc/norm.R, vignettes/scuttle/inst/doc/qc.R dependsOnMe: scater, scran, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: BASiCS, batchelor, DropletUtils, FLAMES, imcRtools, mia, mumosa, muscat, scDblFinder, singleCellTK, spatialHeatmap, velociraptor, mixhvg suggestsMe: bluster, miloR, SingleR, splatter, TSCAN, HCAData, MouseThymusAgeing, SingleRBook, Platypus linksToMe: DropletUtils, scran dependencyCount: 39 Package: SDAMS Version: 1.16.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 1b4da742b7e96a929d407c44e7fbd46e NeedsCompilation: no Title: Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: RELEASE_3_15 git_last_commit: d2240b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SDAMS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SDAMS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SDAMS_1.16.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 60 Package: sechm Version: 1.4.1 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, seriation, ComplexHeatmap, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: e60f17f5d431f965466089679eabff05 NeedsCompilation: no Title: sechm: Complex Heatmaps from a SummarizedExperiment Description: sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: RELEASE_3_15 git_last_commit: a886a33 git_last_commit_date: 2022-04-29 Date/Publication: 2022-04-29 source.ver: src/contrib/sechm_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/sechm_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/sechm_1.4.1.tgz vignettes: vignettes/sechm/inst/doc/sechm.html vignetteTitles: sechm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sechm/inst/doc/sechm.R importsMe: SEtools dependencyCount: 68 Package: segmenter Version: 1.2.0 Depends: R (>= 4.1) Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges, S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils, methods, chromhmmData Suggests: testthat, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz License: GPL-3 MD5sum: cb783f2a0c73b8dc50d471ba2f2b994c NeedsCompilation: no Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM Description: Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation. biocViews: Software, HistoneModification Author: Mahmoud Ahmed [aut, cre] () Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues git_url: https://git.bioconductor.org/packages/segmenter git_branch: RELEASE_3_15 git_last_commit: f9cf94d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/segmenter_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/segmenter_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/segmenter_1.2.0.tgz vignettes: vignettes/segmenter/inst/doc/segmenter.html vignetteTitles: Chromatin Segmentation Analysis Using segmenter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmenter/inst/doc/segmenter.R dependencyCount: 190 Package: segmentSeq Version: 2.30.0 Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 4d174f8d4ac348748c0e226cb275f4bc NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_15 git_last_commit: 1f7dab1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/segmentSeq_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/segmentSeq_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/segmentSeq_2.30.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 55 Package: selectKSigs Version: 1.8.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: fd36867d57d4e7b79fd4c185c931e645 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: RELEASE_3_15 git_last_commit: 3e24671 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/selectKSigs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/selectKSigs_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/selectKSigs_1.8.0.tgz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 126 Package: SELEX Version: 1.28.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: 01ab3b1fc7818f6d79c7d60060c22ec3 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: RELEASE_3_15 git_last_commit: ad6af30 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SELEX_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SELEX_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SELEX_1.28.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 19 Package: SemDist Version: 1.30.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) Archs: x64 MD5sum: 78523b3213e55e0b17870f9fd8725adc NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: RELEASE_3_15 git_last_commit: 2445287 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SemDist_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SemDist_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SemDist_1.30.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 49 Package: semisup Version: 1.20.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 668718f056d22848f0b5e469c0aa7442 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: RELEASE_3_15 git_last_commit: b99728d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/semisup_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/semisup_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/semisup_1.20.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: SEPIRA Version: 1.16.0 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 MD5sum: dbc83243cdf303df1c41a6ff2c18c0cc NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_15 git_last_commit: 13c5b92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SEPIRA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SEPIRA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SEPIRA_1.16.0.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R dependencyCount: 8 Package: seq2pathway Version: 1.28.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: f6245f55e9f563ae81cb0c777a6b3619 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Arjun Kinstlick git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: RELEASE_3_15 git_last_commit: 84a98a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seq2pathway_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seq2pathway_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seq2pathway_1.28.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R dependencyCount: 130 Package: seqArchR Version: 1.0.0 Depends: R (>= 4.2.0) Imports: utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods, stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2 (>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings, grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1) Suggests: cowplot, hopach (>= 2.42.0), BiocStyle, knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>= 0.3.0) License: GPL-3 | file LICENSE MD5sum: 40c69088290274b33b8e0fe6b98d2542 NeedsCompilation: no Title: Identify Different Architectures of Sequence Elements Description: seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos. biocViews: MotifDiscovery, GeneRegulation, MathematicalBiology, SystemsBiology, Transcriptomics, Genetics, Clustering, DimensionReduction, FeatureExtraction, DNASeq Author: Sarvesh Nikumbh [aut, cre, cph] () Maintainer: Sarvesh Nikumbh URL: https://snikumbh.github.io/seqArchR/, https://github.com/snikumbh/seqArchR SystemRequirements: Python (>= 3.5), scikit-learn (>= 0.21.2) VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchR/issues git_url: https://git.bioconductor.org/packages/seqArchR git_branch: RELEASE_3_15 git_last_commit: f2f1672 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqArchR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqArchR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqArchR_1.0.0.tgz vignettes: vignettes/seqArchR/inst/doc/seqArchR.html vignetteTitles: Example usage of _seqArchR_ on simulated DNA sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqArchR/inst/doc/seqArchR.R dependencyCount: 86 Package: SeqArray Version: 1.36.3 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: 2d361544cd783f8445918dfd1ae70665 NeedsCompilation: yes Title: Data management of large-scale whole-genome sequence variant calls Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_15 git_last_commit: 7378309 git_last_commit_date: 2022-09-01 Date/Publication: 2022-09-01 source.ver: src/contrib/SeqArray_1.36.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqArray_1.36.3.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqArray_1.36.3.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SAIGEgds, SeqVarTools importsMe: GBScleanR, GDSArray, GENESIS, ggmanh, VariantExperiment, GMMAT, MAGEE suggestsMe: DelayedDataFrame, HIBAG, VCFArray dependencyCount: 21 Package: seqbias Version: 1.44.0 Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods LinkingTo: Rhtslib (>= 1.15.3) Suggests: Rsamtools, ggplot2 License: LGPL-3 MD5sum: 7f80e6ac3a78d6d65613cce92a668999 NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/seqbias git_branch: RELEASE_3_15 git_last_commit: 59a8fc9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqbias_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqbias_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqbias_1.44.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON dependencyCount: 20 Package: seqCAT Version: 1.18.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE Archs: x64 MD5sum: 1f2b2fc35bc692a61004b313a96163a3 NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: RELEASE_3_15 git_last_commit: ed9e2ad git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqCAT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqCAT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqCAT_1.18.0.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 115 Package: seqCNA Version: 1.42.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 Archs: x64 MD5sum: a0cb01f31c102e88d852f11c6b39495a NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools git_url: https://git.bioconductor.org/packages/seqCNA git_branch: RELEASE_3_15 git_last_commit: f8159d3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqCNA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqCNA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqCNA_1.42.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R dependencyCount: 26 Package: seqcombo Version: 1.18.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 9e27ee0e1dcc738707a0ba33468daa5a NeedsCompilation: no Title: Visualization Tool for Genetic Reassortment Description: Provides useful functions for visualizing virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: RELEASE_3_15 git_last_commit: 5040afc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqcombo_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqcombo_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqcombo_1.18.0.tgz vignettes: vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 38 Package: SeqGate Version: 1.6.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) Archs: x64 MD5sum: eda5fe66c0af45bddd4f95c6e1d90012 NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: RELEASE_3_15 git_last_commit: dc9ced8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SeqGate_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqGate_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqGate_1.6.0.tgz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 26 Package: SeqGSEA Version: 1.36.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 8bac35fce612dd96e354449e01a3dc18 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: RELEASE_3_15 git_last_commit: 503888f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SeqGSEA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqGSEA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqGSEA_1.36.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 113 Package: seqLogo Version: 1.62.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: 6d8609e95050b89cc73551cc940866df NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] () Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: RELEASE_3_15 git_last_commit: f2d0b53 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqLogo_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqLogo_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqLogo_1.62.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: rGADEM, generegulation importsMe: igvR, IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, MAGAR, motifcounter, MotifDb, universalmotif, phangorn dependencyCount: 4 Package: seqPattern Version: 1.28.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: 8562ae781acf97e806538d1429b8d21c NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: RELEASE_3_15 git_last_commit: 689a170 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqPattern_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqPattern_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqPattern_1.28.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 21 Package: seqsetvis Version: 1.16.0 Depends: R (>= 3.6), ggplot2 Imports: cowplot, data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 978fc399b9fabd19c01621487250e94b NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: RELEASE_3_15 git_last_commit: e3338b3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqsetvis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqsetvis_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqsetvis_1.16.0.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 91 Package: SeqSQC Version: 1.18.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: fe96e72135a997b1f3be1923c8b61904 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: RELEASE_3_15 git_last_commit: 92da15a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SeqSQC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqSQC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqSQC_1.18.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 139 Package: seqTools Version: 1.30.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: b7e59cd2bbf7c68a340765c9883aabba NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers git_url: https://git.bioconductor.org/packages/seqTools git_branch: RELEASE_3_15 git_last_commit: 67aee00 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/seqTools_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqTools_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqTools_1.30.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R importsMe: qckitfastq dependencyCount: 3 Package: SeqVarTools Version: 1.34.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 MD5sum: d0b6cb4a99ca9ed45e038faedbbf3999 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: RELEASE_3_15 git_last_commit: 5506e93 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SeqVarTools_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqVarTools_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqVarTools_1.34.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS, VariantExperiment, GMMAT, MAGEE dependencyCount: 72 Package: sesame Version: 1.14.2 Depends: R (>= 4.1), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, illuminaio, MASS, wheatmap (>= 0.2.0), GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache, GenomeInfoDb, stats, SummarizedExperiment, dplyr, reshape2 Suggests: scales, knitr, DNAcopy, e1071, randomForest, RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel, grDevices, pals License: MIT + file LICENSE Archs: x64 MD5sum: 7b4d2d27da7293df07d81840f2d7272b NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays.SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: RELEASE_3_15 git_last_commit: 67b0bdb git_last_commit_date: 2022-05-17 Date/Publication: 2022-05-19 source.ver: src/contrib/sesame_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/sesame_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/sesame_1.14.2.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/KYCG.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", "5. knowYourCG", 3. Modeling, 2. Non-human Array, 1. Quality Control, "0. Basic Usage" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/inferences.R, vignettes/sesame/inst/doc/KYCG.R, vignettes/sesame/inst/doc/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: MethReg, TCGAbiolinksGUI suggestsMe: RnBeads, TCGAbiolinks, sesameData dependencyCount: 135 Package: SEtools Version: 1.10.0 Depends: R (>= 4.0) Imports: BiocParallel, Matrix, SummarizedExperiment, DESeq2, S4Vectors, data.table, edgeR, openxlsx, stats, sva, sechm Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: 34a99f340ebc83338e5151eff191b5ec NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of tools for working with the SummarizedExperiment class, including merging, melting, aggregation functions. Plotting functions historically in this package have been moved to the sechm package. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: RELEASE_3_15 git_last_commit: 3951246 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SEtools_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SEtools_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SEtools_1.10.0.tgz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 121 Package: sevenbridges Version: 1.26.0 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: f45f0558f23c8d3591c8ff281f28b476 NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Soner Koc [aut, cre], Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Seven Bridges Genomics [cph, fnd] Maintainer: Soner Koc URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_15 git_last_commit: 845f2c8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sevenbridges_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sevenbridges_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sevenbridges_1.26.0.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 26 Package: sevenC Version: 1.16.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: 4d2505809c7b458a861581ed34fe8fa0 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: RELEASE_3_15 git_last_commit: f85ff45 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sevenC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sevenC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sevenC_1.16.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 75 Package: SGSeq Version: 1.30.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: e88ba70629deceaaf3f85f0ec2bd1194 NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: RELEASE_3_15 git_last_commit: a19e395 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SGSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SGSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SGSeq_1.30.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 dependencyCount: 99 Package: SharedObject Version: 1.10.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 8d174dfb6e7d3ca8b6fbdb48be08da0a NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: RELEASE_3_15 git_last_commit: 6e231d6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SharedObject_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SharedObject_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SharedObject_1.10.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html, vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStartChinese, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R, vignettes/SharedObject/inst/doc/quick_start_guide.R importsMe: NewWave dependencyCount: 7 Package: shinyepico Version: 1.4.2 Depends: R (>= 4.2.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>= 1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>= 1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>= 4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>= 2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets (>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0), shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData License: AGPL-3 + file LICENSE MD5sum: 12b99339e28d2a48fe6a235cbd092761 NeedsCompilation: no Title: ShinyÉPICo Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis. biocViews: DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl Author: Octavio Morante-Palacios [cre, aut] Maintainer: Octavio Morante-Palacios URL: https://github.com/omorante/shiny_epico VignetteBuilder: knitr BugReports: https://github.com/omorante/shiny_epico/issues git_url: https://git.bioconductor.org/packages/shinyepico git_branch: RELEASE_3_15 git_last_commit: 731c058 git_last_commit_date: 2022-05-18 Date/Publication: 2022-05-19 source.ver: src/contrib/shinyepico_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/shinyepico_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/shinyepico_1.4.2.tgz vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html vignetteTitles: shinyepico hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R dependencyCount: 205 Package: shinyMethyl Version: 1.32.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 Archs: x64 MD5sum: 8d612d12e8de860354c8f8f6260daf97 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_15 git_last_commit: eedc4ce git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/shinyMethyl_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/shinyMethyl_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/shinyMethyl_1.32.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 156 Package: ShortRead Version: 1.54.0 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 MD5sum: fc2196c41f70001aba4d0f6b4e108e18 NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_15 git_last_commit: a1082a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ShortRead_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ShortRead_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ShortRead_1.54.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing, SimRAD, STRMPS importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, MACPET, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, genBaRcode suggestsMe: BiocParallel, CSAR, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq dependencyCount: 49 Package: SIAMCAT Version: 2.0.1 Depends: R (>= 3.6.0), mlr3, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot, lmerTest, mlr3learners, mlr3tuning, paradox, lgr Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 MD5sum: e8fe5ed7a17ca185f453e5181c887532 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (), Jakob Wirbel [aut, cre] (), Georg Zeller [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: RELEASE_3_15 git_last_commit: 7757183 git_last_commit_date: 2022-09-29 Date/Publication: 2022-10-02 source.ver: src/contrib/SIAMCAT_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIAMCAT_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SIAMCAT_2.0.1.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing, SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 130 Package: SICtools Version: 1.26.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: 34ba2e1717455c57b035c0fd12240221 NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: RELEASE_3_15 git_last_commit: 3cfeba1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SICtools_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/SICtools_1.26.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 40 Package: SigCheck Version: 2.28.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 Archs: x64 MD5sum: 115d0b73451512ad63ca4a61b996e3af NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: RELEASE_3_15 git_last_commit: 77cae73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SigCheck_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigCheck_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigCheck_2.28.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 122 Package: sigFeature Version: 1.14.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL (>= 2) MD5sum: 5da7a554df0c8e1877712d90f1565031 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: RELEASE_3_15 git_last_commit: 5c73b87 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sigFeature_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigFeature_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sigFeature_1.14.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.html vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 64 Package: SigFuge Version: 1.34.0 Depends: R (>= 3.5.0), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: e96362af16bc48cc1120da911396f63f NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: RELEASE_3_15 git_last_commit: e11eca8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SigFuge_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigFuge_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigFuge_1.34.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R dependencyCount: 53 Package: siggenes Version: 1.70.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: f46fb0791fce5ffa2794d905d0ec114b NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: RELEASE_3_15 git_last_commit: c263daa git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/siggenes_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/siggenes_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/siggenes_1.70.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: minfi, trio, XDE, DeSousa2013, INCATome suggestsMe: GCSscore, logicFS dependencyCount: 16 Package: sights Version: 1.22.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 6a9d28573d393a8524dffa4b377f8a7c NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: RELEASE_3_15 git_last_commit: 1f2e786 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sights_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sights_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sights_1.22.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 43 Package: signatureSearch Version: 1.10.0 Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray, BiocGenerics LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, org.Hs.eg.db, signatureSearchData, DT License: Artistic-2.0 MD5sum: 3d0d3accfb43137c51f1b9d1d4747d41 NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut] Maintainer: Brendan Gongol URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: RELEASE_3_15 git_last_commit: 4ec2b4d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/signatureSearch_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/signatureSearch_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/signatureSearch_1.10.0.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R dependencyCount: 177 Package: signeR Version: 1.22.0 Depends: VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, graphics, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: e438ef76dc83b47acfbacc3d97f750bb NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_15 git_last_commit: 5b34ce0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/signeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/signeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/signeR_1.22.0.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html vignetteTitles: signeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R dependencyCount: 151 Package: sigPathway Version: 1.64.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 Archs: x64 MD5sum: f919ed93c8aee1f6f458c7143713a4c1 NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html git_url: https://git.bioconductor.org/packages/sigPathway git_branch: RELEASE_3_15 git_last_commit: 68f60c1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sigPathway_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigPathway_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sigPathway_1.64.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome dependencyCount: 0 Package: SigsPack Version: 1.10.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 665b83010bff1df8846f7a3f588feadb NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: RELEASE_3_15 git_last_commit: 6e539d8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SigsPack_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigsPack_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigsPack_1.10.0.tgz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 100 Package: sigsquared Version: 1.28.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 Archs: x64 MD5sum: 778b7426cfa9af842c77386cab8a0f91 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: RELEASE_3_15 git_last_commit: 7ddf76a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sigsquared_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigsquared_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sigsquared_1.28.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 12 Package: SIM Version: 1.66.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: ebc9658ae40813ec36e84659af3c4dc7 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: RELEASE_3_15 git_last_commit: 458303e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SIM_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIM_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIM_1.66.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 59 Package: SIMAT Version: 1.28.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 510fc9bd4f1fa5e4762207df6f525a34 NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_15 git_last_commit: 985ca73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SIMAT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMAT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMAT_1.28.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 48 Package: SimBindProfiles Version: 1.34.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 Archs: x64 MD5sum: c3b8c1e0760493a512fba0a3cffd529b NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer git_url: https://git.bioconductor.org/packages/SimBindProfiles git_branch: RELEASE_3_15 git_last_commit: 7334808 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SimBindProfiles_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SimBindProfiles_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SimBindProfiles_1.34.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R dependencyCount: 83 Package: SIMD Version: 1.14.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: 5162fab121ddfbb7e3fe68a5de0b9d64 NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: RELEASE_3_15 git_last_commit: ecb9510 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SIMD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMD_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMD_1.14.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 13 Package: SimFFPE Version: 1.8.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 44bae146739b2f6212fe8dcdccf69cfd NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei [aut, cre] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: RELEASE_3_15 git_last_commit: 810877d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SimFFPE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SimFFPE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SimFFPE_1.8.0.tgz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 50 Package: similaRpeak Version: 1.28.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: e02ed241c2b691661199ca455c0360bd NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: RELEASE_3_15 git_last_commit: 59a27d6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/similaRpeak_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/similaRpeak_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/similaRpeak_1.28.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R suggestsMe: metagene dependencyCount: 2 Package: SIMLR Version: 1.22.0 Depends: R (>= 4.1.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: a8c1f577e428fff41f38e4294c290430 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [cre, aut] (), Bo Wang [aut], Luca De Sano [aut] (), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: RELEASE_3_15 git_last_commit: 6a75b84 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SIMLR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMLR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMLR_1.22.0.tgz vignettes: vignettes/SIMLR/inst/doc/vignette.pdf vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR}) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/vignette.R importsMe: SingleCellSignalR dependencyCount: 14 Package: simplifyEnrichment Version: 1.6.1 Depends: R (>= 3.6.0), BiocGenerics, grid Imports: GOSemSim, ComplexHeatmap (>= 2.7.4), circlize, GetoptLong, digest, tm, GO.db, org.Hs.eg.db, AnnotationDbi, slam, methods, clue, grDevices, graphics, stats, utils, proxyC, Matrix, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, clusterProfiler, msigdbr, DOSE, DO.db, reactome.db, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: b0bbbc4e6f6ed49fb16a209fd8fc1d09 NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: RELEASE_3_15 git_last_commit: 42b964d git_last_commit_date: 2022-06-11 Date/Publication: 2022-06-12 source.ver: src/contrib/simplifyEnrichment_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/simplifyEnrichment_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/simplifyEnrichment_1.6.1.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html vignetteTitles: A Shiny app to interactively visualize clustering results, Simplify Functional Enrichment Results, Word Cloud Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/simplifyEnrichment/inst/doc/interactive.R, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.R, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.R suggestsMe: cola, InteractiveComplexHeatmap, scITD dependencyCount: 76 Package: sincell Version: 1.28.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Archs: x64 MD5sum: b6e2e4e6a401338666b8bd2b132146c8 NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: RELEASE_3_15 git_last_commit: 970404e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sincell_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sincell_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sincell_1.28.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R importsMe: ctgGEM dependencyCount: 61 Package: single Version: 1.0.0 Depends: R (>= 4.1) Imports: Biostrings, BiocGenerics, dplyr, GenomicAlignments,IRanges, methods, reshape2, rlang, Rsamtools, stats, stringr, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ebac37bde82e33f93f833b81eaf85ea1 NeedsCompilation: no Title: Accurate consensus sequence from nanopore reads of a gene library Description: Accurate consensus sequence from nanopore reads of a DNA gene library. SINGLe corrects for systematic errors in nanopore sequencing reads of gene libraries and it retrieves true consensus sequences of variants identified by a barcode, needing only a few reads per variant. More information in preprint doi: https://doi.org/10.1101/2020.03.25.007146. biocViews: Software, Sequencing Author: Rocio Espada [aut, cre] () Maintainer: Rocio Espada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/single git_branch: RELEASE_3_15 git_last_commit: 0778987 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/single_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/single_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/single_1.0.0.tgz vignettes: vignettes/single/inst/doc/single.html vignetteTitles: single hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/single/inst/doc/single.R dependencyCount: 64 Package: SingleCellExperiment Version: 1.18.1 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 Archs: x64 MD5sum: e06f75e378d063c87e8564874f708b4c NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb] Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_15 git_last_commit: db7768a git_last_commit_date: 2022-09-30 Date/Publication: 2022-10-02 source.ver: src/contrib/SingleCellExperiment_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleCellExperiment_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleCellExperiment_1.18.1.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html, vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Applying over a SingleCellExperiment object, 3. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R, vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: BASiCS, batchelor, BayesSpace, CATALYST, celda, CellBench, CelliD, CellTrails, CHETAH, clusterExperiment, cydar, cytomapper, DropletUtils, ExperimentSubset, iSEE, LoomExperiment, MAST, mia, mumosa, NeuCA, POWSC, scAlign, scAnnotatR, scater, scGPS, schex, scPipe, scran, scuttle, singleCellTK, SpatialExperiment, splatter, switchde, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scRNAseq, TENxBrainData, TENxPBMCData, TMExplorer, OSCA.intro, DIscBIO, imcExperiment importsMe: ADImpute, aggregateBioVar, airpart, ANCOMBC, APL, ASURAT, bayNorm, BEARscc, BUSseq, ccfindR, CellMixS, Cepo, ChromSCape, CiteFuse, clustifyr, CoGAPS, conclus, condiments, corral, destiny, DifferentialRegulation, Dino, distinct, dittoSeq, escape, EWCE, fcoex, FEAST, fishpond, FLAMES, ggspavis, GSVA, HIPPO, ILoReg, imcRtools, infercnv, IRISFGM, iSEEu, LineagePulse, mbkmeans, MetaNeighbor, miloR, miQC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, peco, phemd, pipeComp, SC3, SCArray, scBFA, scCB2, sccomp, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, scone, scp, scReClassify, scruff, scry, scTensor, scTGIF, scTreeViz, slalom, slingshot, Spaniel, spatialHeatmap, SPOTlight, SPsimSeq, standR, tradeSeq, traviz, treekoR, UCell, VAExprs, velociraptor, waddR, zellkonverter, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, digitalDLSorteR, mixhvg, SC.MEB, SCRIP, scTEP suggestsMe: CellaRepertorium, cellxgenedp, DEsingle, FCBF, genomicInstability, hca, HDF5Array, InteractiveComplexHeatmap, M3Drop, MOFA2, ontoProc, phenopath, progeny, PubScore, QFeatures, scFeatureFilter, scPCA, scRecover, SingleR, TREG, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, dyngen, Platypus, RaceID, Seurat, singleCellHaystack dependencyCount: 25 Package: SingleCellSignalR Version: 1.8.0 Depends: R (>= 4.0) Imports: BiocManager, circlize, limma, igraph, gplots, grDevices, edgeR, SIMLR, data.table, pheatmap, stats, Rtsne, graphics, stringr, foreach, multtest, scran, utils, Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 6adea885203e10833262fcbb950fd662 NeedsCompilation: no Title: Cell Signalling Using Single Cell RNAseq Data Analysis Description: Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions. biocViews: SingleCell, Network, Clustering, RNASeq, Classification Author: Simon Cabello-Aguilar [aut], Jacques Colinge [cre, aut] Maintainer: Jacques Colinge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: RELEASE_3_15 git_last_commit: b0e29be git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SingleCellSignalR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleCellSignalR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleCellSignalR_1.8.0.tgz vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R suggestsMe: tidySingleCellExperiment, scDiffCom dependencyCount: 96 Package: singleCellTK Version: 2.6.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix, matrixStats, methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, SoupX, sva, reshape2, shinyalert, circlize, enrichR, celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, fishpond, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN, TrajectoryUtils, generics, scuttle, utils, stats Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling, org.Mm.eg.db, stringr, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0) License: MIT + file LICENSE MD5sum: d140de0b13300b6e5e1ddd17dd5a2f56 NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology, BatchEffect, Normalization, QualityControl, DataImport, GUI Author: Yichen Wang [aut, cre] (), Irzam Sarfraz [aut] (), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], David Jenkins [aut] (), Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut] (), Joshua David Campbell [aut] Maintainer: Yichen Wang URL: https://www.camplab.net/sctk/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_15 git_last_commit: b6fc536 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/singleCellTK_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/singleCellTK_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/singleCellTK_2.6.0.tgz vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html vignetteTitles: 1. Introduction to singleCellTK hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R suggestsMe: celda dependencyCount: 369 Package: SingleMoleculeFootprinting Version: 1.4.0 Depends: R (>= 4.1.0) Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb, GenomicRanges, data.table, grDevices, plyr, IRanges, RColorBrewer, stats, QuasR Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, parallel, rmarkdown, readr, SingleMoleculeFootprintingData, testthat (>= 3.0.0) License: GPL-3 MD5sum: fb1b09872a644522138cc51d1ad82dbc NeedsCompilation: no Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting is an R package providing functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location biocViews: DNAMethylation, Coverage, NucleosomePositioning, DataRepresentation, Epigenetics, MethylSeq, QualityControl Author: Guido Barzaghi [aut, cre] (), Arnaud Krebs [aut] (), Mike Smith [ctb] () Maintainer: Guido Barzaghi VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues git_url: https://git.bioconductor.org/packages/SingleMoleculeFootprinting git_branch: RELEASE_3_15 git_last_commit: 2aa5441 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SingleMoleculeFootprinting_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleMoleculeFootprinting_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleMoleculeFootprinting_1.4.0.tgz vignettes: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.html vignetteTitles: SingleMoleculeFootprinting hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.R dependencyCount: 113 Package: SingleR Version: 1.10.0 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocNeighbors, BiocParallel, BiocSingular, stats, utils, Rcpp, beachmat, parallel LinkingTo: Rcpp, beachmat Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scater, scran, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 + file LICENSE MD5sum: 9134d8a90954f8f4f97d14c252bfda03 NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/LTLA/SingleR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_15 git_last_commit: ecb1892 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SingleR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleR_1.10.0.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: singleCellTK suggestsMe: tidySingleCellExperiment, SingleRBook, tidyseurat dependencyCount: 44 Package: singscore Version: 1.16.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: hexbin, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 598560a3c4d48e3b912d7c6f7299fff3 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: RELEASE_3_15 git_last_commit: 8be0ad1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/singscore_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/singscore_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/singscore_1.16.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: TBSignatureProfiler, SingscoreAMLMutations, clustermole suggestsMe: vissE, msigdb dependencyCount: 115 Package: SISPA Version: 1.26.0 Depends: R (>= 3.5),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 Archs: x64 MD5sum: 503c44adbc01146df7fc1f7b833abced NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SISPA git_branch: RELEASE_3_15 git_last_commit: 5db4c30 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SISPA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SISPA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SISPA_1.26.0.tgz vignettes: vignettes/SISPA/inst/doc/SISPA.html vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SISPA/inst/doc/SISPA.R dependencyCount: 108 Package: sitadela Version: 1.4.0 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, utils Suggests: BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 MD5sum: f3d8cc8032c363b0d1191d6c0152e1e5 NeedsCompilation: no Title: An R package for the easy provision of simple but complete tab-delimited genomic annotation from a variety of sources and organisms Description: Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported. biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing, Transcriptomics, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/sitadela VignetteBuilder: knitr BugReports: https://github.com/pmoulos/sitadela/issues git_url: https://git.bioconductor.org/packages/sitadela git_branch: RELEASE_3_15 git_last_commit: 4d09305 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sitadela_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sitadela_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sitadela_1.4.0.tgz vignettes: vignettes/sitadela/inst/doc/sitadela.html vignetteTitles: Building a simple annotation database hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sitadela/inst/doc/sitadela.R dependencyCount: 97 Package: sitePath Version: 1.12.0 Depends: R (>= 4.1) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 76ea71603a29a43ddcc62aefbe776477 NeedsCompilation: yes Title: Phylogeny-based sequence clustering with site polymorphism Description: Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: RELEASE_3_15 git_last_commit: c20f75e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sitePath_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sitePath_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sitePath_1.12.0.tgz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 65 Package: sizepower Version: 1.66.0 Depends: stats License: LGPL MD5sum: 0fc8a16ef80e649558aeb7be0ae73f8e NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: RELEASE_3_15 git_last_commit: 2308e44 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sizepower_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sizepower_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sizepower_1.66.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: skewr Version: 1.28.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Archs: x64 MD5sum: 648cb514c7eae772a9ec02ef775cbdd1 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: RELEASE_3_15 git_last_commit: fd2672c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/skewr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/skewr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/skewr_1.28.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 173 Package: slalom Version: 1.18.0 Depends: R (>= 4.0) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat License: GPL-2 MD5sum: 9ab0663655ced41fb3bae0fcfcd239f1 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome, KEGG Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: RELEASE_3_15 git_last_commit: c0e3048 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/slalom_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/slalom_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/slalom_1.18.0.tgz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 84 Package: slingshot Version: 2.4.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: e26ca4172794cc1c7c7e5efd46dc54ed NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (, rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: RELEASE_3_15 git_last_commit: fbf2297 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/slingshot_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/slingshot_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/slingshot_2.4.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology: Comparing Conditions along a Trajectory, Slingshot: Trajectory Inference for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R dependsOnMe: OSCA.advanced importsMe: condiments, tradeSeq, traviz suggestsMe: Platypus, RaceID dependencyCount: 33 Package: SLqPCR Version: 1.62.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) Archs: x64 MD5sum: 293b4e25d6ff43f4f45b455030636b37 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: RELEASE_3_15 git_last_commit: cb45dbd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SLqPCR_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SLqPCR_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SLqPCR_1.62.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R dependencyCount: 1 Package: SMAD Version: 1.12.0 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 481cd5e313d1fa667a385327de8c93a8 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to prey proteins captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: RELEASE_3_15 git_last_commit: a6f2d26 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SMAD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMAD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SMAD_1.12.0.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 28 Package: SMAP Version: 1.60.0 Depends: R (>= 2.10), methods License: GPL-2 MD5sum: 9dcb4effca6abcc7ac8389f00e29e028 NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson git_url: https://git.bioconductor.org/packages/SMAP git_branch: RELEASE_3_15 git_last_commit: 7f844fd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SMAP_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMAP_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SMAP_1.60.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R dependencyCount: 1 Package: SMITE Version: 1.24.0 Depends: R (>= 3.5), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 76e6413f9332da5a07fb6c5a7713a315 NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_15 git_last_commit: 2ec1f99 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SMITE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMITE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SMITE_1.24.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 148 Package: SNAGEE Version: 1.36.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 Archs: x64 MD5sum: 0f188e7527abca2747c1ecaa84253b15 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: RELEASE_3_15 git_last_commit: b953975 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SNAGEE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNAGEE_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNAGEE_1.36.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapCGH Version: 1.66.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL MD5sum: dc99b0ffe3dd291c0eee7148a538667c NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni git_url: https://git.bioconductor.org/packages/snapCGH git_branch: RELEASE_3_15 git_last_commit: d4cca59 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/snapCGH_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snapCGH_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snapCGH_1.66.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP dependencyCount: 94 Package: snapcount Version: 1.8.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 302d386bbd6ec08c25d6bb94d833e2b5 NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles [aut, cre] Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: RELEASE_3_15 git_last_commit: 9dc9a5f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/snapcount_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snapcount_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snapcount_1.8.0.tgz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 41 Package: snifter Version: 1.6.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 1b5399e315bcb8e94af3cbff64b8a71a NeedsCompilation: no Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: RELEASE_3_15 git_last_commit: 6beffae git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/snifter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snifter_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snifter_1.6.0.tgz vignettes: vignettes/snifter/inst/doc/snifter.html vignetteTitles: Introduction to snifter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snifter/inst/doc/snifter.R dependsOnMe: OSCA.advanced suggestsMe: scater dependencyCount: 25 Package: snm Version: 1.44.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 0c7b4c9b026d66cf4394d065f548fe04 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: RELEASE_3_15 git_last_commit: f5ae027 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/snm_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snm_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snm_1.44.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: edge, ExpressionNormalizationWorkflow dependencyCount: 51 Package: SNPediaR Version: 1.22.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 473fe600c8b945bb2bc43cadb176afcb NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: RELEASE_3_15 git_last_commit: 5a530ab git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SNPediaR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPediaR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPediaR_1.22.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.26.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: e129fa411d4a8d561ccff778b38caf1b NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: christian.arnold@embl.de git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_15 git_last_commit: 7c587a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SNPhood_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPhood_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPhood_1.26.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 128 Package: SNPRelate Version: 1.30.1 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: b2f64523c8d0fd977415cea32df5212e NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_15 git_last_commit: baef8a7 git_last_commit_date: 2022-05-13 Date/Publication: 2022-05-15 source.ver: src/contrib/SNPRelate_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPRelate_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPRelate_1.30.1.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.46.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 MD5sum: 2fc7e360a882f194b9daad16b97a78b9 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: RELEASE_3_15 git_last_commit: 1e70784 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/snpStats_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snpStats_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snpStats_1.46.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: MAGAR importsMe: DExMA, GeneGeneInteR, gwascat, ldblock, martini, RVS, scoreInvHap, GenomicTools.fileHandler, GWASbyCluster, LDheatmap, PhenotypeSimulator, snpEnrichment, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, omicRexposome, omicsPrint, VariantAnnotation, adjclust, coloc, dartR, genio, pegas, statgenGWAS dependencyCount: 12 Package: soGGi Version: 1.28.0 Depends: R (>= 3.5.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) Archs: x64 MD5sum: 4ba142aae437fc12dc4043c8cdc6245b NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: RELEASE_3_15 git_last_commit: 1015d59 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/soGGi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/soGGi_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/soGGi_1.28.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: profileplyr dependencyCount: 88 Package: sojourner Version: 1.10.0 Imports: ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: d1c0ef1d4499b76b6a49a2845a2b55a4 NeedsCompilation: no Title: Statistical analysis of single molecule trajectories Description: Single molecule tracking has evolved as a novel new approach complementing genomic sequencing, it reports live biophysical properties of molecules being investigated besides properties relating their coding sequence; here we provided "sojourner" package, to address statistical and bioinformatic needs related to the analysis and comprehension of high throughput single molecule tracking data. biocViews: Technology, WorkflowStep Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen [ctb], Sojourner Developer [cre] Maintainer: Sojourner Developer URL: https://github.com/sheng-liu/sojourner VignetteBuilder: knitr BugReports: https://github.com/sheng-liu/sojourner/issues git_url: https://git.bioconductor.org/packages/sojourner git_branch: RELEASE_3_15 git_last_commit: 077ecb3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sojourner_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sojourner_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sojourner_1.10.0.tgz vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html vignetteTitles: Sojourner: an R package for statistical analysis of single molecule trajectories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R dependencyCount: 101 Package: SomaticSignatures Version: 2.32.0 Depends: R (>= 3.5.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE MD5sum: 41cd768750f37c251b70a678216b8165 NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: RELEASE_3_15 git_last_commit: 444d376 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SomaticSignatures_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SomaticSignatures_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SomaticSignatures_2.32.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: YAPSA dependencyCount: 168 Package: SOMNiBUS Version: 1.4.0 Depends: R (>= 4.1.0) Imports: graphics, Matrix, mgcv, stats, VGAM Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: a61fcb6a491240228beae27b8165c092 NeedsCompilation: no Title: Smooth modeling of bisulfite sequencing Description: This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture. biocViews: DNAMethylation, Regression, Epigenetics, DifferentialMethylation, Sequencing, FunctionalPrediction Author: Kaiqiong Zhao [aut], Kathleen Klein [cre] Maintainer: Kathleen Klein URL: https://github.com/kaiqiong/SOMNiBUS VignetteBuilder: knitr BugReports: https://github.com/kaiqiong/SOMNiBUS/issues git_url: https://git.bioconductor.org/packages/SOMNiBUS git_branch: RELEASE_3_15 git_last_commit: a498f57 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SOMNiBUS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SOMNiBUS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SOMNiBUS_1.4.0.tgz vignettes: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.html vignetteTitles: Analyzing Targeted Bisulfite Sequencing data with SOMNiBUS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.R dependencyCount: 13 Package: SpacePAC Version: 1.34.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: a73c8275a83fcb73ff4ea0448c559983 NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: RELEASE_3_15 git_last_commit: 612472f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SpacePAC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpacePAC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpacePAC_1.34.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC dependencyCount: 30 Package: Spaniel Version: 1.10.0 Depends: R (>= 4.0) Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr, methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg, magrittr, utils, S4Vectors, DropletUtils, jsonlite, png Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: f23971bd5a07af77d9db662f9373847d NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen [aut, cre] Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: RELEASE_3_15 git_last_commit: 957db89 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Spaniel_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Spaniel_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Spaniel_1.10.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html vignetteTitles: Spaniel 10X Visium hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R dependencyCount: 198 Package: sparrow Version: 1.2.0 Depends: R (>= 4.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, magrittr, matrixStats, msigdbr (>= 7.4.1), KernSmooth, knitr, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE MD5sum: 97b467cbce87215641e6dee9d322d7e8 NeedsCompilation: no Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (), Arkadiusz Gladki [ctb], Denali Therapeutics [fnd] (2018+), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues git_url: https://git.bioconductor.org/packages/sparrow git_branch: RELEASE_3_15 git_last_commit: 51adea9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sparrow_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparrow_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparrow_1.2.0.tgz vignettes: vignettes/sparrow/inst/doc/sparrow.html vignetteTitles: Performing gene set enrichment analyses with sparrow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparrow/inst/doc/sparrow.R suggestsMe: gCrisprTools dependencyCount: 130 Package: sparseDOSSA Version: 1.20.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: 66444ddeee402530855100971a05149a NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseDOSSA git_branch: RELEASE_3_15 git_last_commit: d640521 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sparseDOSSA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparseDOSSA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparseDOSSA_1.20.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R dependencyCount: 25 Package: sparseMatrixStats Version: 1.8.0 Depends: MatrixGenerics (>= 1.5.3) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 0e750bdad4edd1d4d655a68ff88b6945 NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/sparseMatrixStats SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_15 git_last_commit: 4f1e221 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sparseMatrixStats_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparseMatrixStats_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparseMatrixStats_1.8.0.tgz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: atena, DelayedMatrixStats, GSVA, adjclust suggestsMe: MatrixGenerics, scPCA dependencyCount: 11 Package: sparsenetgls Version: 1.14.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: d05211943e1b2a5cf489d98d846112ae NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: RELEASE_3_15 git_last_commit: 11e00dc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sparsenetgls_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparsenetgls_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparsenetgls_1.14.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 23 Package: SparseSignatures Version: 2.6.0 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2 Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE Archs: x64 MD5sum: 38ca4942cceb9c614591c7fc40444982 NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [cre, aut] (), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [aut] (), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_15 git_last_commit: 7b7bfb9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SparseSignatures_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SparseSignatures_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SparseSignatures_2.6.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R dependencyCount: 95 Package: SpatialCPie Version: 1.12.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 78b5e5cc5c28d402146db148f01646fd NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: RELEASE_3_15 git_last_commit: 6837e78 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SpatialCPie_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialCPie_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialCPie_1.12.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 110 Package: spatialDE Version: 1.2.0 Depends: R (>= 4.1) Imports: reticulate, basilisk, checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, S4Vectors, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 5e2c4864b821d82bd8d8071c1d5908b7 NeedsCompilation: no Title: R wrapper for SpatialDE Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk. biocViews: Software, Transcriptomics Author: Davide Corso [aut, cre] (), Milan Malfait [aut] (), Lambda Moses [aut] () Maintainer: Davide Corso URL: https://github.com/sales-lab/spatialDE, https://bioconductor.org/packages/spatialDE/ VignetteBuilder: knitr BugReports: https://github.com/sales-lab/spatialDE/issues git_url: https://git.bioconductor.org/packages/spatialDE git_branch: RELEASE_3_15 git_last_commit: af03d9e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spatialDE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatialDE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spatialDE_1.2.0.tgz vignettes: vignettes/spatialDE/inst/doc/spatialDE.html vignetteTitles: Introduction to spatialDE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialDE/inst/doc/spatialDE.R dependencyCount: 121 Package: SpatialDecon Version: 1.6.0 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix Suggests: testthat, knitr, rmarkdown, qpdf License: MIT + file LICENSE MD5sum: 4d634a7beb28b3bcc71312c6869af342 NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics Author: Maddy Griswold [cre, aut], Patrick Danaher [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_15 git_last_commit: d5fa19e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SpatialDecon_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialDecon_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialDecon_1.6.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_ST.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a Spatial Transcriptomics dataset, Use of SpatialDecon in a small GeoMx dataet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_ST.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R suggestsMe: GeomxTools dependencyCount: 134 Package: SpatialExperiment Version: 1.6.1 Depends: methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, DropletUtils, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix License: GPL-3 MD5sum: c7b108ad3c611b666172595fcd692d3e NeedsCompilation: no Title: S4 Class for Spatially Resolved Transcriptomics Data Description: Defines an S4 class for storing data from spatially resolved transcriptomics (ST) experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based ST platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. biocViews: DataRepresentation, DataImport, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L. Crowell [aut], Lukas M. Weber [aut] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpatialExperiment VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_15 git_last_commit: fed53c6 git_last_commit_date: 2022-08-07 Date/Publication: 2022-08-09 source.ver: src/contrib/SpatialExperiment_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialExperiment_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialExperiment_1.6.1.tgz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Introduction to the SpatialExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: ExperimentSubset, imcRtools, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData importsMe: ggspavis, nnSVG, spatialDE, standR, SingleCellMultiModal suggestsMe: GeomxTools, SPOTlight dependencyCount: 93 Package: spatialHeatmap Version: 2.2.0 Depends: R (>= 3.5.0) Imports: av, BiocParallel, BiocFileCache, data.table, DESeq2, distinct, edgeR, WGCNA, flashClust, htmlwidgets, genefilter, ggplot2, ggdendro, grImport, grid, gridExtra, gplots, igraph, HDF5Array, limma, methods, magick, Matrix, rsvg, shiny, dynamicTreeCut, grDevices, graphics, ggplotify, parallel, plotly, pROC, rols, rappdirs, reshape2, scater, scuttle, scran, stats, SummarizedExperiment, SingleCellExperiment, shinydashboard, S4Vectors, utils, visNetwork, UpSetR, xml2, yaml Suggests: knitr, rmarkdown, BiocStyle, BiocSingular, RUnit, BiocGenerics, ExpressionAtlas, DT, Biobase, GEOquery, shinyWidgets, shinyjs, htmltools, shinyBS, sortable License: Artistic-2.0 MD5sum: 4ef0b2b6cf973210322592f039bb3a4d NeedsCompilation: no Title: spatialHeatmap Description: The spatialHeatmap package provides functionalities for visualizing cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. biocViews: Spatial, Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Jordan Hayes [aut], Le Zhang [aut], Bing Yang [aut], Wolf Frommer [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues git_url: https://git.bioconductor.org/packages/spatialHeatmap git_branch: RELEASE_3_15 git_last_commit: b145080 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spatialHeatmap_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatialHeatmap_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spatialHeatmap_2.2.0.tgz vignettes: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html vignetteTitles: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Network Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R dependencyCount: 217 Package: spatzie Version: 1.2.0 Depends: R (>= 4.1) Imports: BiocGenerics, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicInteractions, GenomicRanges, ggplot2, IRanges, matrixStats, motifmatchr, S4Vectors, stats, SummarizedExperiment, TFBSTools, utils Suggests: BiocManager, Biostrings, knitr, pheatmap, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 MD5sum: cacda6f379d28481300aff7e144a653f NeedsCompilation: no Title: Identification of enriched motif pairs from chromatin interaction data Description: Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC, Transcription Author: Jennifer Hammelman [aut, cre, cph] (), Konstantin Krismer [aut] (), David Gifford [ths, cph] () Maintainer: Jennifer Hammelman URL: https://spatzie.mit.edu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spatzie git_branch: RELEASE_3_15 git_last_commit: 0c97407 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spatzie_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatzie_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spatzie_1.2.0.tgz vignettes: vignettes/spatzie/inst/doc/individual-steps.html, vignettes/spatzie/inst/doc/single-call.html vignetteTitles: YY1 ChIA-PET motif analysis (step-by-step), YY1 ChIA-PET motif analysis (single call) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 173 Package: specL Version: 1.30.0 Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.5), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 3781d165bb7a14a03e9030ab3d967412 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (), Jonas Grossmann [aut] (), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: RELEASE_3_15 git_last_commit: 4f0c06d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/specL_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/specL_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/specL_1.30.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf, vignettes/specL/inst/doc/report.html vignetteTitles: Introduction to specL, Automatic Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 30 Package: SpeCond Version: 1.50.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 51f88872d065df6ca29cc18154818aeb NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: RELEASE_3_15 git_last_commit: fd93613 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SpeCond_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpeCond_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpeCond_1.50.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 46 Package: Spectra Version: 1.6.0 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.25.1) Imports: methods, IRanges, MsCoreUtils (>= 1.7.5), graphics, grDevices, stats, tools, utils, fs, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr (>= 1.0.0) License: Artistic-2.0 MD5sum: bd1fb51be78c7690694b7b276237ec6b NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Jan Stanstrup [ctb] () Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: RELEASE_3_15 git_last_commit: 36a4f90 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Spectra_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Spectra_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Spectra_1.6.0.tgz vignettes: vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader importsMe: CompoundDb, MetaboAnnotation, MobilityTransformR suggestsMe: MetNet, PSMatch, xcms dependencyCount: 26 Package: SpectralTAD Version: 1.12.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: 133e22f8d0b4eb7cc72a06b6097af0e3 NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , John Stansfield , Mikhail Dozmorov Maintainer: Kellen Cresswell URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: RELEASE_3_15 git_last_commit: d75a42f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SpectralTAD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpectralTAD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpectralTAD_1.12.0.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 98 Package: SPEM Version: 1.36.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: f81ec467fde39d5a49b1dba6fd2a58d9 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: RELEASE_3_15 git_last_commit: 7583296 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SPEM_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPEM_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPEM_1.36.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 9 Package: SPIA Version: 2.48.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 34d9ee5b24763919e97160dc962e95bb NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: RELEASE_3_15 git_last_commit: c85864b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SPIA_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPIA_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPIA_2.48.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 14 Package: spicyR Version: 1.8.0 Depends: R (>= 4.1) Imports: ggplot2, concaveman, BiocParallel, spatstat.core, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr, scam Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: c61697644c406951c3a776a37334f33c NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: spicyR provides a series of functions to aid in the analysis of both immunofluorescence and mass cytometry imaging data as well as other assays that can deeply phenotype individual cells and their spatial location. biocViews: SingleCell, CellBasedAssays Author: Nicolas Canete [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: RELEASE_3_15 git_last_commit: 854bdfc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spicyR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spicyR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spicyR_1.8.0.tgz vignettes: vignettes/spicyR/inst/doc/segmentedCells.html, vignettes/spicyR/inst/doc/spicy.html vignetteTitles: "Introduction to SegmentedCells", "Introduction to spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/segmentedCells.R, vignettes/spicyR/inst/doc/spicy.R importsMe: lisaClust dependencyCount: 109 Package: SpidermiR Version: 1.26.1 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, gdata Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 320ac9fd35b32483191b6dad46cfba10 NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MATADOR database. biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues git_url: https://git.bioconductor.org/packages/SpidermiR git_branch: RELEASE_3_15 git_last_commit: 8ffa688 git_last_commit_date: 2022-10-10 Date/Publication: 2022-10-11 source.ver: src/contrib/SpidermiR_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpidermiR_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SpidermiR_1.26.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: StarBioTrek dependencyCount: 62 Package: spikeLI Version: 2.56.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: df03dbfe9020839ba179e276d9841c6b NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: RELEASE_3_15 git_last_commit: f6e5b38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spikeLI_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spikeLI_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spikeLI_2.56.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.2.0 Depends: Rsamtools, GenomicRanges, R (>= 3.6.0) Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings, GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome, S4Vectors, graphics, ggplot2, utils Suggests: covr, testthat, equatiomatic, universalmotif, kebabs, ComplexHeatmap, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: 47c29ba4bdb453941fb568a7f00383f5 NeedsCompilation: no Title: Spike-in calibration for cell-free MeDIP Description: spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms. biocViews: DifferentialMethylation, DNAMethylation, Normalization, Preprocessing, QualityControl, Sequencing Author: Samantha Wilson [aut], Jordan Veldboom [ctb], Lauren Harmon [aut], Tim Triche [aut, cre] Maintainer: Tim Triche URL: https://github.com/trichelab/spiky VignetteBuilder: knitr BugReports: https://github.com/trichelab/spiky/issues git_url: https://git.bioconductor.org/packages/spiky git_branch: RELEASE_3_15 git_last_commit: 9300a49 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spiky_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spiky_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spiky_1.2.0.tgz vignettes: vignettes/spiky/inst/doc/spiky_vignette.html vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in controls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R dependencyCount: 82 Package: spkTools Version: 1.52.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 45ecdee4f4cd508b577a2884d52b6c26 NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: RELEASE_3_15 git_last_commit: d9baf5c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spkTools_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spkTools_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spkTools_1.52.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 9 Package: splatter Version: 1.20.0 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.15.16), stats, SummarizedExperiment, utils, crayon, S4Vectors, grDevices Suggests: BiocStyle, covr, cowplot, magick, knitr, limSolve, lme4, progress, pscl, testthat, preprocessCore, rmarkdown, scDD, scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC, BiocManager, spelling, igraph, scuttle, BiocSingular, VariantAnnotation, Biostrings, GenomeInfoDb, GenomicRanges, IRanges License: GPL-3 + file LICENSE MD5sum: 69fa4beeb7c045ac9c449c0bf25564b7 NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (), Belinda Phipson [aut] (), Christina Azodi [ctb] (), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_15 git_last_commit: e60b5a4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/splatter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/splatter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/splatter_1.20.0.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatPop.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, splatPop simulation, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatPop.R, vignettes/splatter/inst/doc/splatter.R importsMe: bcTSNE, SCRIP suggestsMe: NewWave, scone, scPCA, SummarizedBenchmark dependencyCount: 92 Package: SplicingFactory Version: 1.4.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: 4a059f8048c69ccf8b276e43ca2eb00d NeedsCompilation: no Title: Splicing Diversity Analysis for Transcriptome Data Description: The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions. biocViews: Transcriptomics, RNASeq, DifferentialSplicing, AlternativeSplicing, TranscriptomeVariant Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut, cre] () Maintainer: Endre Sebestyen URL: https://github.com/esebesty/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/esebesty/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: RELEASE_3_15 git_last_commit: 99571e7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SplicingFactory_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SplicingFactory_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SplicingFactory_1.4.0.tgz vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html vignetteTitles: SplicingFactory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R dependencyCount: 25 Package: SplicingGraphs Version: 1.36.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 198bcaa8ff4f3d6960f3341af499990a NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: RELEASE_3_15 git_last_commit: 7151ba0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SplicingGraphs_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SplicingGraphs_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SplicingGraphs_1.36.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 100 Package: splineTimeR Version: 1.24.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: 578a89d3f2dabff004eed0caf71305f7 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: RELEASE_3_15 git_last_commit: 970d612 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/splineTimeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/splineTimeR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/splineTimeR_1.24.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 63 Package: SPLINTER Version: 1.22.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 2711248272ba5f0b00d2823b51607613 NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: RELEASE_3_15 git_last_commit: b0553c0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SPLINTER_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPLINTER_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPLINTER_1.22.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 150 Package: splots Version: 1.62.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI License: LGPL MD5sum: 4aeafd49b95a8b72ad878d62202b4272 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is provided to support legacy code and reverse dependencies, but it should not be used as a dependency for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or generic ggplot2 graphics functionality. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_15 git_last_commit: b193d65 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/splots_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/splots_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/splots_1.62.0.tgz vignettes: vignettes/splots/inst/doc/splots.html vignetteTitles: splots: visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splots.R dependsOnMe: cellHTS2, HD2013SGI importsMe: RNAinteract dependencyCount: 2 Package: SPONGE Version: 1.18.1 Depends: R (>= 3.6) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse, caret, dplyr, biomaRt, randomForest, ggridges, cvms, miRBaseConverter, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: 748c629ff4b59001c7ef62afd22a0a4d NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression, RandomForest, MachineLearning, Author: Markus List [aut, cre] (), Markus Hoffmann [aut] () Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_15 git_last_commit: e7a1311 git_last_commit_date: 2022-04-29 Date/Publication: 2022-04-29 source.ver: src/contrib/SPONGE_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPONGE_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SPONGE_1.18.1.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html, vignettes/SPONGE/inst/doc/spongEffects.html vignetteTitles: SPONGE vignette, spongEffects vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R, vignettes/SPONGE/inst/doc/spongEffects.R importsMe: miRspongeR suggestsMe: mirTarRnaSeq dependencyCount: 232 Package: SPOTlight Version: 1.0.0 Depends: R (>= 4.1) Imports: ggplot2, NMF, Matrix, matrixStats, nnls, SingleCellExperiment, stats Suggests: BiocStyle, colorBlindness, ExperimentHub, DelayedArray, ggcorrplot, grid, igraph, jpeg, knitr, methods, png, rmarkdown, scater, scatterpie, scran, Seurat, SeuratObject, SpatialExperiment, SummarizedExperiment, S4Vectors, TabulaMurisSenisData, TENxVisiumData, testthat License: GPL-3 MD5sum: d486371f6243afe26210ead7d1627726 NeedsCompilation: no Title: `SPOTlight`: Spatial Transcriptomics Deconvolution Description: `SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). biocViews: SingleCell, Spatial, StatisticalMethod Author: Marc Elosua-Bayes [aut, cre], Helena L. Crowell [aut] Maintainer: Marc Elosua-Bayes URL: https://github.com/MarcElosua/SPOTlight VignetteBuilder: knitr BugReports: https://github.com/MarcElosua/SPOTlight/issues git_url: https://git.bioconductor.org/packages/SPOTlight git_branch: RELEASE_3_15 git_last_commit: a584808 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SPOTlight_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPOTlight_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPOTlight_1.0.0.tgz vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html vignetteTitles: "SPOTlight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R dependencyCount: 74 Package: spqn Version: 1.8.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 MD5sum: 7eac787310869a1aff35d178730d32f5 NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: RELEASE_3_15 git_last_commit: ac65abe git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/spqn_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spqn_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spqn_1.8.0.tgz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 54 Package: SPsimSeq Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 MD5sum: 7557c15adc1ceed9b1c7c9928fb3c013 NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: RELEASE_3_15 git_last_commit: 87f7290 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SPsimSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPsimSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPsimSeq_1.6.0.tgz vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html vignetteTitles: Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R suggestsMe: benchdamic dependencyCount: 136 Package: SQLDataFrame Version: 1.10.3 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors (>= 0.33.3), Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: 696d343b672eab4c4eef57631b5f63f6 NeedsCompilation: no Title: Representation of SQL database in DataFrame metaphor Description: SQLDataFrame is developed to lazily represent and efficiently analyze SQL-based tables in _R_. SQLDataFrame supports common and familiar 'DataFrame' operations such as '[' subsetting, rbind, cbind, etc.. The internal implementation is based on the widely adopted dplyr grammar and SQL commands. In-memory datasets or plain text files (.txt, .csv, etc.) could also be easily converted into SQLDataFrames objects (which generates a new database on-disk). biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre] (), Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: RELEASE_3_15 git_last_commit: 82d7dbf git_last_commit_date: 2022-10-10 Date/Publication: 2022-10-11 source.ver: src/contrib/SQLDataFrame_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/SQLDataFrame_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.2/SQLDataFrame_1.10.3.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R dependencyCount: 39 Package: SQUADD Version: 1.46.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 655e612f98d85db1692fbfca46504644 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html git_url: https://git.bioconductor.org/packages/SQUADD git_branch: RELEASE_3_15 git_last_commit: b1cb673 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SQUADD_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SQUADD_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SQUADD_1.46.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R dependencyCount: 6 Package: sRACIPE Version: 1.12.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture License: MIT + file LICENSE MD5sum: a180eaa1ec4ec66be62678d22318e80d NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Vivek Kohar [aut, cre] (), Mingyang Lu [aut] Maintainer: Vivek Kohar URL: https://vivekkohar.github.io/sRACIPE/, https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: RELEASE_3_15 git_last_commit: db88b92 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sRACIPE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sRACIPE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sRACIPE_1.12.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 82 Package: SRAdb Version: 1.58.1 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 4b14d6c74d8276d669d493cb03a23228 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_15 git_last_commit: 0e0b86c git_last_commit_date: 2022-08-29 Date/Publication: 2022-08-30 source.ver: src/contrib/SRAdb_1.58.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SRAdb_1.58.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SRAdb_1.58.1.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R suggestsMe: parathyroidSE dependencyCount: 58 Package: srnadiff Version: 1.16.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, baySeq, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocStyle, BiocManager LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 Archs: x64 MD5sum: 62cc4cd041441fceef660643e9010e35 NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: RELEASE_3_15 git_last_commit: bbc1f73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/srnadiff_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/srnadiff_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/srnadiff_1.16.0.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 212 Package: sscore Version: 1.68.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 0453c84951a3c3e302e6873af26bc669 NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html git_url: https://git.bioconductor.org/packages/sscore git_branch: RELEASE_3_15 git_last_commit: c4d4edb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sscore_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sscore_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sscore_1.68.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R dependencyCount: 12 Package: sscu Version: 2.26.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: a077d49cd265b4a5d51388c943a5a7e4 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: RELEASE_3_15 git_last_commit: 152a4b3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sscu_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sscu_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sscu_2.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 27 Package: sSeq Version: 1.34.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: b283027a53d547f90ca107489ec7637b NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: RELEASE_3_15 git_last_commit: 882bea1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sSeq_1.34.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq suggestsMe: NBLDA dependencyCount: 3 Package: ssize Version: 1.70.0 Depends: gdata, xtable License: LGPL MD5sum: c6fa00778ac95c4c1a17736ae0213e36 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: RELEASE_3_15 git_last_commit: b52d2a3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ssize_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssize_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssize_1.70.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: maGUI dependencyCount: 6 Package: sSNAPPY Version: 1.0.2 Depends: R (>= 4.2.0) Imports: dplyr, magrittr, rlang, stats, plyr, purrr, BiocParallel, graphite, Rcpp, tibble, ggplot2, ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment, edgeR, methods LinkingTo: Rcpp, RcppArmadillo Suggests: BiocManager, BiocStyle, cowplot, DT, knitr, pander, rmarkdown, spelling, stringr, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 4652e62763e9c5b88d3f85526e97327c NeedsCompilation: yes Title: Single Sample directioNAl Pathway Perturbation analYsis Description: A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential directions of changes.Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels. biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling Author: Wenjun Liu [aut, cre] () Maintainer: Wenjun Liu URL: https://wenjun-liu.github.io/sSNAPPY/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues git_url: https://git.bioconductor.org/packages/sSNAPPY git_branch: RELEASE_3_15 git_last_commit: 0e451a9 git_last_commit_date: 2022-08-02 Date/Publication: 2022-08-07 source.ver: src/contrib/sSNAPPY_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/sSNAPPY_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/sSNAPPY_1.0.2.tgz vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html vignetteTitles: Single Sample Directional Pathway Perturbation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R dependencyCount: 115 Package: ssPATHS Version: 1.10.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: fab3b92b16d317e85cb4b1e3960bcefb NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: RELEASE_3_15 git_last_commit: 7e2b2cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ssPATHS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssPATHS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssPATHS_1.10.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 116 Package: ssrch Version: 1.12.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: ad8a1b515bd848c4ae2cf30786355434 NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: RELEASE_3_15 git_last_commit: 889d64b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ssrch_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssrch_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssrch_1.12.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R importsMe: HumanTranscriptomeCompendium dependencyCount: 42 Package: ssviz Version: 1.30.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 MD5sum: b2704ac685fef449af42f7f028e3c8bf NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: RELEASE_3_15 git_last_commit: 1c87d4e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ssviz_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssviz_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssviz_1.30.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 64 Package: stageR Version: 1.18.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: ac177810fa59f3f1e708e50cbadffa03 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_15 git_last_commit: e5087a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/stageR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/stageR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/stageR_1.18.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg, satuRn dependencyCount: 25 Package: STAN Version: 2.24.0 Depends: R (>= 3.5.0), methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) MD5sum: 59651243e93181b82af13829050ce157 NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STAN git_branch: RELEASE_3_15 git_last_commit: bbd31d4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/STAN_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STAN_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STAN_2.24.0.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R dependencyCount: 149 Package: standR Version: 1.0.0 Depends: R (>= 4.1) Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment, SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2, tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics, grDevices, stats, methods, mclustcomp Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggalluvial, ggpubr, ggrepel, cluster, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 2def201c51a4e8edaaa00a9206250040 NeedsCompilation: no Title: Spatial transcriptome analyses of Nanostring's DSP data in R Description: standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations. biocViews: Spatial, Transcriptomics, GeneExpression, DifferentialExpression, QualityControl, Normalization, ExperimentHubSoftware Author: Ning Liu [aut, cre] (), Dharmesh D Bhuva [aut] (), Ahmed Mohamed [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/standR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/standR/issues git_url: https://git.bioconductor.org/packages/standR git_branch: RELEASE_3_15 git_last_commit: cd44448 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/standR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/standR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/standR_1.0.0.tgz vignettes: vignettes/standR/inst/doc/standR_introduction.html vignetteTitles: standR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/standR/inst/doc/standR_introduction.R dependencyCount: 127 Package: staRank Version: 1.38.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL Archs: x64 MD5sum: 8b83a10c65b08cd4fbf678ffeb3b6a83 NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: ImmunoOncology, MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg git_url: https://git.bioconductor.org/packages/staRank git_branch: RELEASE_3_15 git_last_commit: fd7f97d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/staRank_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/staRank_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/staRank_1.38.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R dependencyCount: 90 Package: StarBioTrek Version: 1.22.0 Depends: R (>= 3.3) Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics, grDevices, igraph, reshape2, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: af87d6b657147f1c2a8b9b24e6e5dee6 NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues git_url: https://git.bioconductor.org/packages/StarBioTrek git_branch: RELEASE_3_15 git_last_commit: 0d5545e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/StarBioTrek_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/StarBioTrek_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/StarBioTrek_1.22.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R dependencyCount: 96 Package: STATegRa Version: 1.32.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: ce322c95e6963d33c68814077f3b8703 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: RELEASE_3_15 git_last_commit: f83c6bd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/STATegRa_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STATegRa_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STATegRa_1.32.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 57 Package: statTarget Version: 1.26.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown License: LGPL (>= 3) MD5sum: 942b88adae24e48a151a1df7447dc2ba NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, QC-RLSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_15 git_last_commit: 2363042 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/statTarget_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/statTarget_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/statTarget_1.26.0.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 30 Package: STdeconvolve Version: 1.0.0 Depends: R (>= 4.1) Imports: topicmodels, BiocParallel, Matrix, methods, mgcv, ggplot2, scatterpie, viridis, slam, stats, clue, liger, reshape2, graphics, grDevices, utils Suggests: knitr, BiocStyle, rmarkdown, testthat, rcmdcheck, gplots, gridExtra, hash, dplyr, parallel License: GPL-3 MD5sum: d52fd5b130b781128b2a1d46f493148d NeedsCompilation: no Title: Reference-free Cell-Type Deconvolution of Multi-Cellular Spatially Resolved Transcriptomics Data Description: STdeconvolve as an unsupervised, reference-free approach to infer latent cell-type proportions and transcriptional profiles within multi-cellular spatially-resolved pixels from spatial transcriptomics (ST) datasets. STdeconvolve builds on latent Dirichlet allocation (LDA), a generative statistical model commonly used in natural language processing for discovering latent topics in collections of documents. In the context of natural language processing, given a count matrix of words in documents, LDA infers the distribution of words for each topic and the distribution of topics in each document. In the context of ST data, given a count matrix of gene expression in multi-cellular ST pixels, STdeconvolve applies LDA to infer the putative transcriptional profile for each cell-type and the proportional representation of each cell-type in each multi-cellular ST pixel. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression Author: Brendan Miller [aut, cre] (), Jean Fan [aut] () Maintainer: Brendan Miller URL: https://jef.works/STdeconvolve/ VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/STdeconvolve/issues git_url: https://git.bioconductor.org/packages/STdeconvolve git_branch: RELEASE_3_15 git_last_commit: d690f59 git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/STdeconvolve_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STdeconvolve_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STdeconvolve_1.0.0.tgz vignettes: vignettes/STdeconvolve/inst/doc/vignette.html vignetteTitles: STdeconvolve Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STdeconvolve/inst/doc/vignette.R dependencyCount: 79 Package: stepNorm Version: 1.68.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 2803bf064c2dfe989ccc8a67cb939c90 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: RELEASE_3_15 git_last_commit: 9eaad10 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/stepNorm_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/stepNorm_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/stepNorm_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: strandCheckR Version: 1.14.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr, rmarkdown Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 45f1c576702dbd6f08635f4a26800fc3 NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Steve Pederson [aut] Maintainer: Thu-Hien To URL: https://github.com/UofABioinformaticsHub/strandCheckR VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/strandCheckR/issues git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_15 git_last_commit: 702d6ed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/strandCheckR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/strandCheckR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/strandCheckR_1.14.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 127 Package: Streamer Version: 1.42.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: 0157bcea534cdc96d138f05615e8ffd7 NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/Streamer git_branch: RELEASE_3_15 git_last_commit: 007dfe7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Streamer_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Streamer_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Streamer_1.42.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy dependencyCount: 10 Package: STRINGdb Version: 2.8.4 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 3145b9f685ec6228a118504569220cfb NeedsCompilation: no Title: STRINGdb - Protein-Protein Interaction Networks and Functional Enrichment Analysis Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_15 git_last_commit: 7573fa2 git_last_commit_date: 2022-05-17 Date/Publication: 2022-05-17 source.ver: src/contrib/STRINGdb_2.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/STRINGdb_2.8.4.zip mac.binary.ver: bin/macosx/contrib/4.2/STRINGdb_2.8.4.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: IMMAN, netZooR, pwOmics, RITAN, XINA, crosstalkr suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 40 Package: STROMA4 Version: 1.20.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 Archs: x64 MD5sum: dabf610785c92e5169d42664c35bf62f NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh git_url: https://git.bioconductor.org/packages/STROMA4 git_branch: RELEASE_3_15 git_last_commit: 73bd2a4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/STROMA4_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STROMA4_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STROMA4_1.20.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R dependencyCount: 18 Package: struct Version: 1.8.0 Depends: R (>= 4.0) Imports: methods,ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors, rols Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: 813bc9e159b6d62050ecd58cfc15b175 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: RELEASE_3_15 git_last_commit: f76175b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/struct_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/struct_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/struct_1.8.0.tgz vignettes: vignettes/struct/inst/doc/struct_templates_and_helper_functions.html vignetteTitles: Introduction to STRUCT - STatistics in R using Class-based Templates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/struct/inst/doc/struct_templates_and_helper_functions.R dependsOnMe: structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 55 Package: Structstrings Version: 1.12.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: b296a63c8f42c9154f5901b5dae99b38 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: RELEASE_3_15 git_last_commit: fb4cdb1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Structstrings_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Structstrings_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Structstrings_1.12.0.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 22 Package: structToolbox Version: 1.8.0 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats, utils Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 MD5sum: 5d3deabf41a7a40a271c4c66a9035c8d NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre] (), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://github.com/computational-metabolomics/structToolbox VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: RELEASE_3_15 git_last_commit: 29adf97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/structToolbox_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/structToolbox_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/structToolbox_1.8.0.tgz vignettes: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html vignetteTitles: Data analysis of metabolomics and other omics datasets using the structToolbox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R suggestsMe: metabolomicsWorkbenchR dependencyCount: 80 Package: StructuralVariantAnnotation Version: 1.12.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment, GenomeInfoDb, Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown, tidyverse, knitr, ggbio, biovizBase, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, License: GPL-3 + file LICENSE Archs: x64 MD5sum: b53641b73a8dcaf1db2bc2679b39d097 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (), Ruining Dong [aut] () Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: RELEASE_3_15 git_last_commit: 3777c65 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/StructuralVariantAnnotation_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/StructuralVariantAnnotation_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/StructuralVariantAnnotation_1.12.0.tgz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependsOnMe: svaNUMT, svaRetro dependencyCount: 99 Package: SubCellBarCode Version: 1.12.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: a7ba8a3d8ef39a9a79dba491629593b5 NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: RELEASE_3_15 git_last_commit: 1cead76 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SubCellBarCode_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SubCellBarCode_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SubCellBarCode_1.12.0.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 123 Package: subSeq Version: 1.26.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: cb54cdd3d63147bb2a565f48378cbbac NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: RELEASE_3_15 git_last_commit: 955eb28 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/subSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/subSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/subSeq_1.26.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 53 Package: SummarizedBenchmark Version: 2.14.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, rmarkdown, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) MD5sum: d38176b0f8d9fa9c4966eaa91dfca6ca NeedsCompilation: no Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes [aut] (), Patrick Kimes [aut, cre] () Maintainer: Patrick Kimes URL: https://github.com/areyesq89/SummarizedBenchmark, http://bioconductor.org/packages/SummarizedBenchmark/ VignetteBuilder: knitr BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_15 git_last_commit: aac0d7a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SummarizedBenchmark_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SummarizedBenchmark_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SummarizedBenchmark_2.14.0.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study: Single-Cell RNA-Seq Simulation, Feature: Error Handling, Feature: Iterative Benchmarking, Feature: Parallelization, SummarizedBenchmark: Class Details, SummarizedBenchmark: Full Case Study, SummarizedBenchmark: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R suggestsMe: benchmarkfdrData2019 dependencyCount: 78 Package: SummarizedExperiment Version: 1.26.1 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.41.5), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), DelayedArray (>= 0.15.10) Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite, rhdf5, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 8e05d10db06b6832b953053f38993823 NeedsCompilation: no Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: RELEASE_3_15 git_last_commit: c8cbd3b git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-29 source.ver: src/contrib/SummarizedExperiment_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SummarizedExperiment_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SummarizedExperiment_1.26.1.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, AllelicImbalance, ASpediaFI, atena, bambu, BDMMAcorrect, BiocSklearn, BioPlex, BiSeq, bnbc, BrainSABER, bsseq, CAGEfightR, celaref, clusterExperiment, compartmap, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, ENmix, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, exomePeak2, ExperimentSubset, ExpressionAtlas, extraChIPs, FEAST, FRASER, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hermes, hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, lefser, lipidr, LoomExperiment, Macarron, made4, MatrixQCvis, MBASED, methrix, methylPipe, MetNet, mia, miaSim, miaViz, minfi, miRmine, moanin, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, NewWave, OUTRIDER, padma, PDATK, PhIPData, profileplyr, qmtools, qsvaR, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scAnnotatR, scGPS, scone, scTreeViz, SDAMS, SeqGate, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, soGGi, spqn, ssPATHS, stageR, SummarizedBenchmark, survtype, tidySummarizedExperiment, TimeSeriesExperiment, TissueEnrich, TNBC.CMS, TREG, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, DREAM4, fission, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, parathyroidSE, restfulSEData, sampleClassifierData, spatialDmelxsim, spqnData, timecoursedata, tuberculosis, DRomics, ordinalbayes importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, alpine, ANCOMBC, animalcules, anota2seq, APAlyzer, apeglm, APL, appreci8R, ASICS, ASURAT, AUCell, autonomics, awst, barcodetrackR, BASiCS, batchelor, BayesSpace, bayNorm, BBCAnalyzer, beer, benchdamic, bigPint, BiocOncoTK, BioNERO, biosigner, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BRGenomics, BUMHMM, BUScorrect, BUSseq, CAGEr, CATALYST, CBEA, cBioPortalData, ccfindR, celda, CelliD, CellMixS, CellTrails, censcyt, Cepo, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR, CNVRanger, CoGAPS, comapr, combi, conclus, condiments, consensusDE, CopyNumberPlots, corral, countsimQC, cydar, CyTOFpower, cytoKernel, cytomapper, DAMEfinder, dasper, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, destiny, DEWSeq, diffcyt, DifferentialRegulation, diffUTR, Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DominoEffect, doppelgangR, doseR, DropletUtils, Dune, easyRNASeq, eisaR, ELMER, ensemblVEP, epialleleR, epigraHMM, epimutacions, epistack, epivizrData, erma, escape, EWCE, FCBF, fcScan, FindIT2, fishpond, FLAMES, GARS, gCrisprTools, GeneTonic, genomicInstability, getDEE2, ggbio, ggspavis, Glimma, glmGamPoi, glmSparseNet, GRaNIE, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva, icetea, ideal, ILoReg, imcRtools, infercnv, INSPEcT, InterMineR, iSEEu, iteremoval, LACE, LineagePulse, lineagespot, lionessR, MADSEQ, MAI, marr, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, MetaboAnnotation, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, MethylAid, methylscaper, methylumi, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, MLSeq, monaLisa, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, MsFeatures, msgbsR, MSPrep, msqrob2, MuData, MultiDataSet, multiOmicsViz, mumosa, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, netSmooth, nnSVG, NormalyzerDE, NxtIRFcore, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, omicsViewer, oncomix, ORFik, OVESEG, PAIRADISE, pairkat, pcaExplorer, peco, PharmacoGx, phemd, phenopath, PhosR, pipeComp, pmp, POMA, POWSC, proActiv, proDA, psichomics, pulsedSilac, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, regionReport, regsplice, rgsepd, rifi, Rmmquant, RNAAgeCalc, RNAsense, roar, RolDE, ropls, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview, SC3, SCArray, scater, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scp, scPipe, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, sechm, segmenter, seqCAT, sesame, SEtools, sigFeature, SigsPack, singscore, slalom, slingshot, snapcount, SNPhood, Spaniel, SpatialCPie, spatialDE, SpatialExperiment, spatialHeatmap, spatzie, splatter, SplicingFactory, srnadiff, sSNAPPY, standR, struct, StructuralVariantAnnotation, supersigs, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, TEKRABber, tenXplore, tidybulk, tidySingleCellExperiment, TOAST, tomoda, ToxicoGx, tradeSeq, TrajectoryUtils, transformGamPoi, TraRe, traviz, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, tscR, TTMap, TVTB, tximeta, UCell, VAExprs, VariantFiltering, vidger, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, easierData, emtdata, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GSE13015, HMP2Data, IHWpaper, MetaGxBreast, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, digitalDLSorteR, DWLS, HeritSeq, IFAA, imcExperiment, microbial, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, RNAseqQC, SC.MEB, SCRIP, scTEP, xQTLbiolinks suggestsMe: AlpsNMR, AnnotationHub, biobroom, BiocPkgTools, cageminer, dcanr, dce, dearseq, decoupleR, DelayedArray, easier, edgeR, EnMCB, epivizr, epivizrChart, esetVis, fobitools, GENIE3, GenomicRanges, globalSeq, gsean, hca, HDF5Array, HPiP, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, MatrixGenerics, MOFA2, MSnbase, ODER, pathwayPCA, philr, podkat, PSMatch, PubScore, RiboProfiling, S4Vectors, scFeatureFilter, semisup, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, TFutils, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, GSE103322, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, clustree, conos, dyngen, lfc, Platypus, polyRAD, RaceID, seqgendiff, Seurat, Signac, singleCellHaystack, volcano3D dependencyCount: 24 Package: Summix Version: 2.2.0 Depends: R (>= 4.1) Imports: nloptr, methods Suggests: rmarkdown, markdown, knitr License: MIT + file LICENSE MD5sum: 10dbb65e3fd21f5e9884bafcf50c7726 NeedsCompilation: no Title: Summix: A method to estimate and adjust for population structure in genetic summary data Description: This package contains the Summix method for estimating and adjusting for ancestry in genetic summary allele frequency data. The function summix estimates reference ancestry proportions using a mixture model. The adjAF function produces ancestry adjusted allele frequencies for an observed sample with ancestry proportions matching a target person or sample. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], Stoneman Haley [aut] Maintainer: Audrey Hendricks VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: RELEASE_3_15 git_last_commit: 52e560c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Summix_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Summix_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Summix_2.2.0.tgz vignettes: vignettes/Summix/inst/doc/Summix.html vignetteTitles: Summix.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Summix/inst/doc/Summix.R dependencyCount: 38 Package: supersigs Version: 1.4.0 Depends: R (>= 4.1) Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang, utils, Biostrings, stats, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, testthat, VariantAnnotation License: GPL-3 MD5sum: 9172114ab4edba52961df50b37a7e201 NeedsCompilation: no Title: Supervised mutational signatures Description: Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife). biocViews: FeatureExtraction, Classification, Regression, Sequencing, WholeGenome, SomaticMutation Author: Albert Kuo [aut, cre] (), Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut] Maintainer: Albert Kuo URL: https://tomasettilab.github.io/supersigs/ VignetteBuilder: knitr BugReports: https://github.com/TomasettiLab/supersigs/issues git_url: https://git.bioconductor.org/packages/supersigs git_branch: RELEASE_3_15 git_last_commit: 672aa7d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/supersigs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/supersigs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/supersigs_1.4.0.tgz vignettes: vignettes/supersigs/inst/doc/supersigs.html vignetteTitles: Using supersigs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supersigs/inst/doc/supersigs.R dependencyCount: 103 Package: supraHex Version: 1.34.0 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 533b3c57bb63001a9ccc120d4309e90b NeedsCompilation: no Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org git_url: https://git.bioconductor.org/packages/supraHex git_branch: RELEASE_3_15 git_last_commit: 1491191 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/supraHex_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/supraHex_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/supraHex_1.34.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R dependsOnMe: dnet importsMe: Pi suggestsMe: OmnipathR, TCGAbiolinks dependencyCount: 48 Package: surfaltr Version: 1.2.2 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: 41ff9f74787e207e5888d8b68b1fde91 NeedsCompilation: no Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr Description: Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette. biocViews: Software, Visualization, DataRepresentation, SplicedAlignment, Alignment, MultipleSequenceAlignment, MultipleComparison Author: Pooja Gangras [aut, cre] (), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: RELEASE_3_15 git_last_commit: 6ce5ac0 git_last_commit_date: 2022-09-09 Date/Publication: 2022-09-11 source.ver: src/contrib/surfaltr_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/surfaltr_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/surfaltr_1.2.2.tgz vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html vignetteTitles: surfaltr_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R dependencyCount: 114 Package: survcomp Version: 1.46.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: 841ed017b8c91ecf5adb28e74855b2bf NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut], Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut], Zhaleh Safikhani [aut] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_15 git_last_commit: 48de0e2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/survcomp_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/survcomp_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/survcomp_1.46.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: metaseqR2, PDATK, pencal, plsRcox, SIGN suggestsMe: glmSparseNet, GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 35 Package: survtype Version: 1.12.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 420ee90dbaf22e59c86c7bd785c5c60e NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: RELEASE_3_15 git_last_commit: aa83de5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/survtype_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/survtype_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/survtype_1.12.0.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 144 Package: Sushi Version: 1.34.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) Archs: x64 MD5sum: d10f78cb9a949915697aa3f4b16be222 NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Sushi git_branch: RELEASE_3_15 git_last_commit: 9a4a68f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Sushi_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Sushi_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Sushi_1.34.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R importsMe: diffloop dependencyCount: 74 Package: sva Version: 3.44.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: ee1b3407f509b54fb45377441e35e791 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_15 git_last_commit: 45ab2c1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sva_3.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sva_3.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sva_3.44.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, crossmeta, CytoTree, DaMiRseq, debrowser, DExMA, doppelgangR, edge, KnowSeq, MBECS, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, cinaR, dSVA, oncoPredict, scITD, seqgendiff suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, SuperLearner dependencyCount: 69 Package: svaNUMT Version: 1.2.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>= 4.0) Imports: assertthat, stringr, dplyr, methods, rlang, GenomeInfoDb, S4Vectors, GenomicFeatures Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: 10ba447f4d9a38d76b62951c4389a2da NeedsCompilation: no Title: NUMT detection from structural variant calls Description: svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Ruining Dong [aut, cre] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: RELEASE_3_15 git_last_commit: 19aac31 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/svaNUMT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/svaNUMT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/svaNUMT_1.2.0.tgz vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html vignetteTitles: svaNUMT Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R dependencyCount: 100 Package: svaRetro Version: 1.2.0 Depends: GenomicRanges, rtracklayer, BiocGenerics, StructuralVariantAnnotation, R (>= 4.0) Imports: VariantAnnotation, assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomicFeatures, GenomeInfoDb, S4Vectors, utils Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges, circlize, tictoc, IRanges, stats, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: e9ae6c0de13a2756ca6948b379a6507c NeedsCompilation: no Title: Retrotransposed transcript detection from structural variants Description: svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation, Coverage, VariantDetection Author: Ruining Dong [aut, cre] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: RELEASE_3_15 git_last_commit: 7ec810b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/svaRetro_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/svaRetro_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/svaRetro_1.2.0.tgz vignettes: vignettes/svaRetro/inst/doc/svaRetro.html vignetteTitles: svaRetro Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R dependencyCount: 100 Package: SWATH2stats Version: 1.26.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: b86984a38b3a049e4b602b9c1e002a39 NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_15 git_last_commit: c0a744d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SWATH2stats_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SWATH2stats_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SWATH2stats_1.26.0.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 91 Package: SwathXtend Version: 2.18.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 85a91546417605ae588502c237ee5b58 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: RELEASE_3_15 git_last_commit: d50b71f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SwathXtend_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SwathXtend_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SwathXtend_2.18.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.22.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 562e72272563e04468925ed8b338c50a NeedsCompilation: no Title: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: RELEASE_3_15 git_last_commit: e938a48 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/swfdr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/swfdr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/swfdr_1.22.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: switchBox Version: 1.32.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: c412b4d05062ada35b1774e72e809469 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: RELEASE_3_15 git_last_commit: ff2e8ee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/switchBox_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/switchBox_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/switchBox_1.32.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R importsMe: PDATK suggestsMe: multiclassPairs dependencyCount: 11 Package: switchde Version: 1.22.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: 3bae546a0f9c5b001c5216b7d377ae7b NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: RELEASE_3_15 git_last_commit: 3d1c659 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/switchde_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/switchde_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/switchde_1.22.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 57 Package: synapsis Version: 1.2.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 02a016127c6133f1eb0439f05b89f55a NeedsCompilation: no Title: An R package to automate the analysis of double-strand break repair during meiosis Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand. biocViews: Software, SingleCell Author: Lucy McNeill [aut, cre, cph] (), Wayne Crismani [rev, ctb] () Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: RELEASE_3_15 git_last_commit: 4c1b2ed git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/synapsis_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/synapsis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/synapsis_1.2.0.tgz vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html vignetteTitles: Using-synapsis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R dependencyCount: 25 Package: synapter Version: 2.20.1 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle, knitr License: GPL-2 MD5sum: 2dedb3513a992888df56adc9f71165ad NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: RELEASE_3_15 git_last_commit: 89c39be git_last_commit_date: 2022-09-27 Date/Publication: 2022-09-27 source.ver: src/contrib/synapter_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/synapter_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/synapter_2.20.1.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R dependsOnMe: synapterdata dependencyCount: 120 Package: synergyfinder Version: 3.4.5 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: 14983c464cf61ad050242d3ab00f5767 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (https://synergyfinderplus.org/) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: https://synergyfinderplus.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_15 git_last_commit: a8b8c40 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-11 source.ver: src/contrib/synergyfinder_3.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/synergyfinder_3.4.5.zip mac.binary.ver: bin/macosx/contrib/4.2/synergyfinder_3.4.5.tgz vignettes: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html vignetteTitles: User tutorial of the SynergyFinder Plus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R dependencyCount: 201 Package: SynExtend Version: 1.8.0 Depends: R (>= 4.1.0), DECIPHER (>= 2.20.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats, parallel, graphics, grDevices Suggests: BiocStyle, knitr, rtracklayer, igraph, markdown, rmarkdown License: GPL-3 MD5sum: 94ab1873ac885152abf5567e33cbf011 NeedsCompilation: no Title: Tools for Working With Synteny Objects Description: Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (), Aidan Lakshman [aut, ctb] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_15 git_last_commit: ada2504 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/SynExtend_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SynExtend_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SynExtend_1.8.0.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.pdf vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 35 Package: synlet Version: 1.26.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: dae82c1ffd066bc8001219df02936f7c NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_15 git_last_commit: ea403a8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/synlet_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/synlet_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/synlet_1.26.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 85 Package: SynMut Version: 1.12.1 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: ca0a4d149a7eff06ffa696a7f7cc74bb NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: RELEASE_3_15 git_last_commit: d504bbb git_last_commit_date: 2022-06-03 Date/Publication: 2022-06-05 source.ver: src/contrib/SynMut_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SynMut_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SynMut_1.12.1.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 31 Package: systemPipeR Version: 2.2.2 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr, magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata, GenomicAlignments, grid, dplyr, testthat, rjson, annotate, AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT, rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools, GenomicFeatures (>= 1.31.3), VariantAnnotation (>= 1.25.11) License: Artistic-2.0 MD5sum: 7a93b181bd776488b52df73627030a99 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, Workflow Author: Thomas Girke Maintainer: Thomas Girke URL: https://systempipe.org/, https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: RELEASE_3_15 git_last_commit: 6aba24c git_last_commit_date: 2022-04-29 Date/Publication: 2022-05-01 source.ver: src/contrib/systemPipeR_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeR_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeR_2.2.2.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, systemPipeR: Workflow and Visualization Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 84 Package: systemPipeShiny Version: 1.6.1 Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps (>= 0.3.2), drawer (>= 0.2) Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite, openssl Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>= 2.2.0), systemPipeRdata (>= 2.0.0), rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.1.0), cicerone License: GPL (>= 3) MD5sum: 74f697107eb01c3e69071cc5c4c4906b NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://systempipe.org/sps, https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: RELEASE_3_15 git_last_commit: 3c3538d git_last_commit_date: 2022-05-25 Date/Publication: 2022-05-26 source.ver: src/contrib/systemPipeShiny_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeShiny_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeShiny_1.6.1.tgz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 123 Package: systemPipeTools Version: 1.4.0 Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr, ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble, magrittr, DT, stats Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), BiocGenerics, Biostrings, methods License: Artistic-2.0 MD5sum: 4c0529be5365b024df01fffc76d932bb NeedsCompilation: no Title: Tools for data visualization Description: systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering, DifferentialExpression, MultidimensionalScaling, PrincipalComponent Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut] Maintainer: Daniela Cassol VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: RELEASE_3_15 git_last_commit: 54bf662 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/systemPipeTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeTools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeTools_1.4.0.tgz vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html vignetteTitles: systemPipeTools: Data Visualizations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R dependencyCount: 133 Package: TADCompare Version: 1.6.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, rGREAT, SpectralTAD License: MIT + file LICENSE MD5sum: 07ab16c1195053c306a15ed8670679ff NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , Mikhail Dozmorov Maintainer: Kellen Cresswell URL: https://github.com/dozmorovlab/TADCompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: RELEASE_3_15 git_last_commit: 14357af git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TADCompare_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TADCompare_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TADCompare_1.6.0.tgz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 153 Package: tanggle Version: 1.2.0 Depends: R (>= 4.1), ggplot2 (>= 2.2.0), ggtree Imports: ape (>= 5.0), phangorn (>= 2.5), utils, methods Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown License: Artistic-2.0 MD5sum: 04574c131dd9627e669ad3cc271e9b5f NeedsCompilation: no Title: Visualization of Phylogenetic Networks Description: Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb] Maintainer: Klaus Schliep URL: https://klausvigo.github.io/tanggle, https://github.com/KlausVigo/tanggle VignetteBuilder: knitr BugReports: https://github.com/KlausVigo/tanggle/issues git_url: https://git.bioconductor.org/packages/tanggle git_branch: RELEASE_3_15 git_last_commit: 7868e34 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tanggle_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tanggle_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tanggle_1.2.0.tgz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html, vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con *ggplot2*, ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R, vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 62 Package: TAPseq Version: 1.8.0 Depends: R (>= 4.0.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer, BiocStyle License: MIT + file LICENSE MD5sum: 6c4f318cba495afa294914411172e02d NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas R. Gschwind [aut, cre] (), Lars Velten [aut] (), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: RELEASE_3_15 git_last_commit: a9faecb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TAPseq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TAPseq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TAPseq_1.8.0.tgz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 100 Package: target Version: 1.10.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 MD5sum: a24f903bd3b8b33bbed2fba471232bad NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: RELEASE_3_15 git_last_commit: 296a33d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/target_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/target_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/target_1.10.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 49 Package: TargetDecoy Version: 1.2.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, mzID, mzR, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: 496bf40ec3715f65148e57b31eb8c1c6 NeedsCompilation: no Title: Diagnostic Plots to Evaluate the Target Decoy Approach Description: A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method. biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization Author: Elke Debrie [aut, cre], Lieven Clement [aut] (), Milan Malfait [aut] () Maintainer: Elke Debrie URL: https://github.com/statOmics/TargetDecoy VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: RELEASE_3_15 git_last_commit: 195cfe1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TargetDecoy_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetDecoy_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetDecoy_1.2.0.tgz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 110 Package: TargetScore Version: 1.34.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 4a223d9ef1fa4e3dc2d3e0d35e85012b NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: RELEASE_3_15 git_last_commit: cfcc1df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TargetScore_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetScore_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetScore_1.34.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 1.52.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 66a1fa0a4f9861e79c17ae9d2c063854 NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza , Jan Lisec, Henning Redestig, Matt Hannah Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: RELEASE_3_15 git_last_commit: a24ad97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TargetSearch_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetSearch_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetSearch_1.52.0.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TarSeqQC Version: 1.25.1 Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: BiocManager, RUnit License: GPL (>=2) MD5sum: e98d9e7a2e4c849aaa734bc04047c8de NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar git_url: https://git.bioconductor.org/packages/TarSeqQC git_branch: master git_last_commit: 9ad3ed8 git_last_commit_date: 2021-11-21 Date/Publication: 2021-11-21 source.ver: src/contrib/TarSeqQC_1.25.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TarSeqQC_1.25.1.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R dependencyCount: 105 Package: TBSignatureProfiler Version: 1.8.0 Depends: R (>= 4.1) Imports: ASSIGN (>= 1.23.1), BiocGenerics, BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, GSVA, magrittr, methods, RColorBrewer, reshape2, rlang, ROCit, S4Vectors, singscore, stats, SummarizedExperiment Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, glmnet, HGNChelper, impute, knitr, lintr, MASS, plyr, pROC, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE MD5sum: 29bb695fe02f9f38199f3bfb51a8969f NeedsCompilation: no Title: Profile RNA-Seq Data Using TB Pathway Signatures Description: Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility. biocViews: GeneExpression, DifferentialExpression Author: Aubrey Odom-Mabey [aut, cre, dtm] (), David Jenkins [aut, org] (), Xutao Wang [aut], Yue Zhao [ctb] (), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Aubrey Odom-Mabey URL: https://github.com/compbiomed/TBSignatureProfiler https://compbiomed.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: RELEASE_3_15 git_last_commit: 0ea7c4c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TBSignatureProfiler_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TBSignatureProfiler_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TBSignatureProfiler_1.8.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 159 Package: TCC Version: 1.36.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 Archs: x64 MD5sum: a5dbed7896013c3d94cae657e9778aed NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_15 git_last_commit: 0295ffc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TCC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCC_1.36.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR, ExpHunterSuite dependencyCount: 105 Package: TCGAbiolinks Version: 2.24.3 Depends: R (>= 4.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>= 1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, AnnotationHub, ExperimentHub, pathview, clusterProfiler, Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid License: GPL (>= 3) MD5sum: 93464e5ad51878b634a8512223d3ec14 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: RELEASE_3_15 git_last_commit: 823bf847 git_last_commit_date: 2022-06-15 Date/Publication: 2022-06-16 source.ver: src/contrib/TCGAbiolinks_2.24.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCGAbiolinks_2.24.3.zip mac.binary.ver: bin/macosx/contrib/4.2/TCGAbiolinks_2.24.3.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/stemness_score.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 11. Stemness score, 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/stemness_score.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: ELMER, MoonlightR, musicatk, TCGAbiolinksGUI, SingscoreAMLMutations, TCGAWorkflow suggestsMe: Rediscover dependencyCount: 113 Package: TCGAbiolinksGUI Version: 1.22.0 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, sesame, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, rmarkdown, pander License: GPL (>= 3) MD5sum: 0ff30a9fccf673a87c82a7b37506c67e NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: RELEASE_3_15 git_last_commit: a486d4d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TCGAbiolinksGUI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCGAbiolinksGUI_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCGAbiolinksGUI_1.22.0.tgz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R dependencyCount: 303 Package: TCGAutils Version: 1.16.1 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: AnnotationHub, BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, httr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox (>= 2.17.4), rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 19026d4d3c418233d6db22449cdb097f NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. biocViews: Software, WorkflowStep, Preprocessing Author: Marcel Ramos [aut, cre] (), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: RELEASE_3_15 git_last_commit: c707a87 git_last_commit_date: 2022-10-11 Date/Publication: 2022-10-11 source.ver: src/contrib/TCGAutils_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCGAutils_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/TCGAutils_1.16.1.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: cBioPortalData, RTCGAToolbox, terraTCGAdata suggestsMe: CNVRanger, dce, glmSparseNet, curatedTCGAData dependencyCount: 108 Package: TCseq Version: 1.20.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: 6e58afdd53fa6ff3b817614e858f23de NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: RELEASE_3_15 git_last_commit: 250aaf3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TCseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCseq_1.20.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R dependencyCount: 79 Package: TDARACNE Version: 1.46.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 239d43e14616dc6cc529a289db28ef9a NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro git_url: https://git.bioconductor.org/packages/TDARACNE git_branch: RELEASE_3_15 git_last_commit: 8a6e2ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TDARACNE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TDARACNE_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TDARACNE_1.46.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R dependencyCount: 12 Package: TEKRABber Version: 1.0.1 Depends: R (>= 4.1) Imports: apeglm, biomaRt, DESeq2, Rcpp (>= 1.0.7), SCBN, SummarizedExperiment, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, ggpubr, rmarkdown, shiny, knitr, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: d72496bdd3def09235e19a5987e46c21 NeedsCompilation: yes Title: An R package estimates the correlations of orthologs and transposable elements between two species Description: TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes. biocViews: DifferentialExpression, Normalization, Transcription, GeneExpression Author: Yao-Chung Chen [aut, cre] (), Katja Nowick [aut] () Maintainer: Yao-Chung Chen URL: https://github.com/ferygood/TEKRABber VignetteBuilder: knitr BugReports: https://github.com/ferygood/TEKRABber/issues git_url: https://git.bioconductor.org/packages/TEKRABber git_branch: RELEASE_3_15 git_last_commit: c36671f git_last_commit_date: 2022-06-09 Date/Publication: 2022-06-09 source.ver: src/contrib/TEKRABber_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TEKRABber_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/TEKRABber_1.0.1.tgz vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html vignetteTitles: TEKRABber hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R dependencyCount: 121 Package: tenXplore Version: 1.18.1 Depends: R (>= 4.0), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 961f6975854b30132d23f03c63bc5e9a NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_15 git_last_commit: 0a807d0 git_last_commit_date: 2022-08-31 Date/Publication: 2022-09-01 source.ver: src/contrib/tenXplore_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tenXplore_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tenXplore_1.18.1.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 118 Package: TEQC Version: 4.18.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) Archs: x64 MD5sum: 52a33db95921c06a42cf738f53e434b3 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Sarah Bonnin git_url: https://git.bioconductor.org/packages/TEQC git_branch: RELEASE_3_15 git_last_commit: 5e80c76 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TEQC_4.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TEQC_4.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TEQC_4.18.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 32 Package: ternarynet Version: 1.40.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: b478f8530f0b4d06810e524c0015965e NeedsCompilation: yes Title: Ternary Network Estimation Description: Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI. biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: RELEASE_3_15 git_last_commit: a1cdafd git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ternarynet_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ternarynet_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ternarynet_1.40.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 21 Package: terraTCGAdata Version: 1.0.0 Depends: R (>= 4.2.0), AnVIL, MultiAssayExperiment Imports: BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: dae657c569e2788c577850d8b48cb7d0 NeedsCompilation: no Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment Description: Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra. biocViews: Software, Infrastructure, DataImport Author: Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos URL: https://github.com/waldronlab/terraTCGAdata VignetteBuilder: knitr BugReports: https://github.com/waldronlab/terraTCGAdata/issues git_url: https://git.bioconductor.org/packages/terraTCGAdata git_branch: RELEASE_3_15 git_last_commit: b557f4e git_last_commit_date: 2022-04-27 Date/Publication: 2022-04-27 source.ver: src/contrib/terraTCGAdata_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/terraTCGAdata_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/terraTCGAdata_1.0.0.tgz vignettes: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.html vignetteTitles: Obtain Terra TCGA data as MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.R dependencyCount: 112 Package: TFARM Version: 1.18.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 Archs: x64 MD5sum: 58bf14d97f419735f265a7938496953b NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: RELEASE_3_15 git_last_commit: e75b5b3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TFARM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFARM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFARM_1.18.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 62 Package: TFBSTools Version: 1.34.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown License: GPL-2 MD5sum: 2ba8706f0244b00dafac1d7d19e26d19 NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: RELEASE_3_15 git_last_commit: 7f8d0cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TFBSTools_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFBSTools_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFBSTools_1.34.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: chromVAR, enrichTF, esATAC, MatrixRider, monaLisa, motifmatchr, motifStack, primirTSS, spatzie suggestsMe: enhancerHomologSearch, MAGAR, MethReg, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 122 Package: TFEA.ChIP Version: 1.16.0 Depends: R (>= 3.5) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: f3d8fecebba12ca77d84ba2f8224e568 NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: RELEASE_3_15 git_last_commit: 701616b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TFEA.ChIP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFEA.ChIP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFEA.ChIP_1.16.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R dependencyCount: 101 Package: TFHAZ Version: 1.18.4 Depends: R (>= 3.5.0) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: e014b02befcbf945ffc89134a739aa72 NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli Maintainer: Gaia Ceddia VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_15 git_last_commit: dfeadcc git_last_commit_date: 2022-09-12 Date/Publication: 2022-09-13 source.ver: src/contrib/TFHAZ_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFHAZ_1.18.4.zip mac.binary.ver: bin/macosx/contrib/4.2/TFHAZ_1.18.4.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 140 Package: TFutils Version: 1.16.0 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 6bc501e5cb78755ebf9d8a95b0d88561 NeedsCompilation: no Title: TFutils Description: This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided. biocViews: Transcriptomics Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: RELEASE_3_15 git_last_commit: 17bac72 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TFutils_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFutils_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFutils_1.16.0.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R dependencyCount: 108 Package: tidybulk Version: 1.8.2 Depends: R (>= 4.1.0) Imports: tibble, readr, dplyr, magrittr, tidyr, stringi, stringr, rlang, purrr, tidyselect, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, GenomicRanges, methods Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, S4Vectors, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional, survminer, tidySummarizedExperiment, markdown, uwot, matrixStats, igraph License: GPL-3 MD5sum: 12ed213c7b586916909f100f890a257f NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidybulk VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidybulk/issues git_url: https://git.bioconductor.org/packages/tidybulk git_branch: RELEASE_3_15 git_last_commit: 7c032cb git_last_commit_date: 2022-09-27 Date/Publication: 2022-09-29 source.ver: src/contrib/tidybulk_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidybulk_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/tidybulk_1.8.2.tgz vignettes: vignettes/tidybulk/inst/doc/comparison_with_base_R.html, vignettes/tidybulk/inst/doc/introduction.html, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.html, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.html vignetteTitles: Comparison with base R, Overview of the tidybulk package, Manuscript code - differential feature abundance, Manuscript code - transcriptional signature identification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_with_base_R.R, vignettes/tidybulk/inst/doc/introduction.R, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.R, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.R dependencyCount: 66 Package: tidySingleCellExperiment Version: 1.6.3 Depends: R (>= 4.0.0), ttservice, SingleCellExperiment Imports: SummarizedExperiment, dplyr, tibble, tidyr, ggplot2, plotly, magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, Matrix, uwot, celldex, dittoSeq, EnsDb.Hsapiens.v86 License: GPL-3 MD5sum: 460baceb51b7e01aa4a0b9c6ff50d907 NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: tidySingleCellExperiment is an adapter that abstracts the 'SingleCellExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse'. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySingleCellExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: RELEASE_3_15 git_last_commit: 20bc0da git_last_commit_date: 2022-05-20 Date/Publication: 2022-05-22 source.ver: src/contrib/tidySingleCellExperiment_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidySingleCellExperiment_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/tidySingleCellExperiment_1.6.3.tgz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R suggestsMe: sccomp dependencyCount: 83 Package: tidySummarizedExperiment Version: 1.6.1 Depends: R (>= 4.0.0), SummarizedExperiment Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, plotly, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown License: GPL-3 MD5sum: 944b0aab18b10417d39e5739159577a6 NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: tidySummarizedExperiment is an adapter that abstracts the 'SummarizedExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: RELEASE_3_15 git_last_commit: 7951bd9 git_last_commit_date: 2022-05-20 Date/Publication: 2022-05-22 source.ver: src/contrib/tidySummarizedExperiment_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidySummarizedExperiment_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tidySummarizedExperiment_1.6.1.tgz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R suggestsMe: tidybulk dependencyCount: 81 Package: tigre Version: 1.50.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 MD5sum: ed6755c01e0891bb20af6acdff89ed26 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: RELEASE_3_15 git_last_commit: c9e9ba3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tigre_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tigre_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tigre_1.50.0.tgz vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 52 Package: TileDBArray Version: 1.6.0 Depends: DelayedArray (>= 0.15.16) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 7111439406463bea3d9256c7e0bb9d93 NeedsCompilation: yes Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: RELEASE_3_15 git_last_commit: 9cda8da git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TileDBArray_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TileDBArray_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TileDBArray_1.6.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R dependencyCount: 23 Package: tilingArray Version: 1.74.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: x64 MD5sum: 579bad9a70bf35e568a87b62ca5a2a6a NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: RELEASE_3_15 git_last_commit: 3117c24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tilingArray_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tilingArray_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tilingArray_1.74.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2, snapCGH dependencyCount: 85 Package: timecourse Version: 1.68.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL Archs: x64 MD5sum: aa2ef4945d4461920432e4853d46a399 NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: RELEASE_3_15 git_last_commit: 3301913 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/timecourse_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/timecourse_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/timecourse_1.68.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 10 Package: timeOmics Version: 1.7.1 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, propr, lmtest, plyr Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 Archs: x64 MD5sum: 6c8c0a32b8b7bf0280af5902799084b2 NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: master git_last_commit: 137df41 git_last_commit_date: 2021-10-26 Date/Publication: 2021-11-03 source.ver: src/contrib/timeOmics_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/timeOmics_1.7.1.zip mac.binary.ver: bin/macosx/contrib/4.2/timeOmics_1.7.2.tgz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R suggestsMe: netOmics dependencyCount: 72 Package: timescape Version: 1.20.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: aae6afd36b973d6ffe0be16e6cfa1cb7 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: RELEASE_3_15 git_last_commit: 0501227 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/timescape_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/timescape_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/timescape_1.20.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 32 Package: TimeSeriesExperiment Version: 1.13.0 Depends: R (>= 4.1), S4Vectors (>= 0.19.23), SummarizedExperiment (>= 1.11.6) Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0), graphics, Hmisc, limma, methods, magrittr, proxy, stats, tibble, tidyr, vegan, viridis, utils Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap, GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db, MASS, RColorBrewer, rmarkdown, UpSetR, License: MIT + file LICENSE MD5sum: 784ef9fff8406f65a2962fbe1db75692 NeedsCompilation: no Title: Analysis for short time-series data Description: TimeSeriesExperiment is a visualization and analysis toolbox for short time course data. The package includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. Additionally, it also provides methods for retrieving enriched pathways. biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression, ImmunoOncology, Transcription, Normalization, DifferentialExpression, PrincipalComponent, Clustering, Visualization, Pathways Author: Lan Huong Nguyen [cre, aut] () Maintainer: Lan Huong Nguyen URL: https://github.com/nlhuong/TimeSeriesExperiment VignetteBuilder: knitr BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues git_url: https://git.bioconductor.org/packages/TimeSeriesExperiment git_branch: master git_last_commit: 33e5983 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/TimeSeriesExperiment_1.13.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TimeSeriesExperiment_1.13.0.tgz vignettes: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.html vignetteTitles: Gene expression time course data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.R dependencyCount: 134 Package: TimiRGeN Version: 1.6.0 Depends: R (>= 4.1), Mfuzz, MultiAssayExperiment Imports: biomaRt, clusterProfiler, dplyr (>= 0.8.4), FreqProf, gtools (>= 3.8.1), gplots, ggdendro, gghighlight, ggplot2, graphics, grDevices, igraph (>= 1.2.4.2), RCy3, readxl, reshape2, rWikiPathways, scales, stats, tidyr (>= 1.0.2), stringr (>= 1.4.0) Suggests: BiocManager, kableExtra, knitr (>= 1.27), org.Hs.eg.db, org.Mm.eg.db, testthat, rmarkdown License: GPL-3 MD5sum: 62d3f30f9897cff6b9ec7eda908b7544 NeedsCompilation: no Title: Time sensitive microRNA-mRNA integration, analysis and network generation tool Description: TimiRGeN (Time Incorporated miR-mRNA Generation of Networks) is a novel R package which functionally analyses and integrates time course miRNA-mRNA differential expression data. This tool can generate small networks within R or export results into cytoscape or pathvisio for more detailed network construction and hypothesis generation. This tool is created for researchers that wish to dive deep into time series multi-omic datasets. TimiRGeN goes further than many other tools in terms of data reduction. Here, potentially hundreds-of-thousands of potential miRNA-mRNA interactions can be whittled down into a handful of high confidence miRNA-mRNA interactions affecting a signalling pathway, across a time course. biocViews: Clustering, miRNA, Network, Pathways, Software, TimeCourse, Visualization Author: Krutik Patel [aut, cre] Maintainer: Krutik Patel URL: https://github.com/Krutik6/TimiRGeN/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/TimiRGeN/issues git_url: https://git.bioconductor.org/packages/TimiRGeN git_branch: RELEASE_3_15 git_last_commit: 55f72bc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-27 source.ver: src/contrib/TimiRGeN_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TimiRGeN_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TimiRGeN_1.6.0.tgz vignettes: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.html vignetteTitles: TimiRGeN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.R dependencyCount: 190 Package: TIN Version: 1.28.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2184bbfc408d100e9581403c5566e600 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: RELEASE_3_15 git_last_commit: 2f8b7b8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TIN_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TIN_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TIN_1.28.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 131 Package: TissueEnrich Version: 1.16.0 Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 0e52e2fa89d70eb9beb3308d7b9a7f4c NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_15 git_last_commit: 0f6e2ea git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TissueEnrich_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TissueEnrich_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TissueEnrich_1.16.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 88 Package: TitanCNA Version: 1.34.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 MD5sum: 5901fa351e0f77ef4ed862ccaeb1c784 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: RELEASE_3_15 git_last_commit: 6cb0738 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TitanCNA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TitanCNA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TitanCNA_1.34.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 102 Package: tkWidgets Version: 1.74.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 39d574a268326302857b15ad1a73d8e5 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: RELEASE_3_15 git_last_commit: 3544681 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tkWidgets_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tkWidgets_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tkWidgets_1.74.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: tLOH Version: 1.4.0 Depends: R (>= 4.0) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: ef5cd45ea9a1d6c4f36937319c073d40 NeedsCompilation: no Title: Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP). biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression, Transcriptomics Author: Michelle Webb [cre, aut], David Craig [aut] Maintainer: Michelle Webb URL: https://github.com/USCDTG/tLOH VignetteBuilder: knitr BugReports: https://github.com/USCDTG/tLOH/issues git_url: https://git.bioconductor.org/packages/tLOH git_branch: RELEASE_3_15 git_last_commit: 0d32a36 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tLOH_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tLOH_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tLOH_1.4.0.tgz vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html vignetteTitles: tLOH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R dependencyCount: 114 Package: TMixClust Version: 1.18.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: 50876fc1691868892c5e951f56f9db9c NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: RELEASE_3_15 git_last_commit: 71f80a7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TMixClust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TMixClust_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TMixClust_1.18.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 30 Package: TNBC.CMS Version: 1.12.0 Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma, GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid, stats, methods Suggests: knitr License: GPL-3 MD5sum: a93bad7e0f630632b8be7a7d085b9585 NeedsCompilation: no Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes Description: This package implements a machine learning-based classifier for the assignment of consensus molecular subtypes to TNBC samples. It also provides functions to summarize genomic and clinical characteristics. biocViews: Classification, Clustering, GeneExpression, GenePrediction, SupportVectorMachine Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TNBC.CMS git_branch: RELEASE_3_15 git_last_commit: 9643760 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TNBC.CMS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TNBC.CMS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TNBC.CMS_1.12.0.tgz vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf vignetteTitles: TNBC.CMS.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R dependencyCount: 176 Package: TnT Version: 1.18.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 MD5sum: 3ee3b08cc9f50524d01714e27ff4ba54 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_15 git_last_commit: e3faca0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TnT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TnT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TnT_1.18.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 35 Package: TOAST Version: 1.10.1 Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog Imports: stats, methods, SummarizedExperiment, corpcor, doParallel, parallel, ggplot2, tidyr, GGally Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix License: GPL-2 MD5sum: 6653b81af78fb9fbe24f486a0ce8900f NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: RELEASE_3_15 git_last_commit: acbb0d2 git_last_commit_date: 2022-08-24 Date/Publication: 2022-08-25 source.ver: src/contrib/TOAST_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TOAST_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/TOAST_1.10.1.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R dependencyCount: 85 Package: tomoda Version: 1.6.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: ec2cca9d13915716c1c72307de065614 NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Clustering, Visualization Author: Wendao Liu [aut, cre] () Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: RELEASE_3_15 git_last_commit: 088ca36 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tomoda_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tomoda_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tomoda_1.6.0.tgz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 73 Package: tomoseqr Version: 1.0.0 Depends: R (>= 4.2) Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr, methods, shiny, BiocFileCache, readr, tools Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: cdf59af41182ea3289e7347f98247b08 NeedsCompilation: no Title: R Package for Analyzing Tomo-seq Data Description: `tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Visualization, Software Author: Ryosuke Matsuzawa [aut, cre] () Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: RELEASE_3_15 git_last_commit: b38e5b5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tomoseqr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tomoseqr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tomoseqr_1.0.0.tgz vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html vignetteTitles: tomoseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R dependencyCount: 74 Package: topconfects Version: 1.12.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2 Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: b729236ffc91e2da4677131dedb465da NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: RELEASE_3_15 git_last_commit: 7b56e38 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/topconfects_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topconfects_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topconfects_1.12.0.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: MetaVolcanoR, weitrix, GeoTcgaData dependencyCount: 38 Package: topdownr Version: 1.18.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.2.10), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.27.5) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 37a1f5a8ddf937190d3522ffe34bc8e2 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (), Pavel Shliaha [aut] (), Ole Nørregaard Jensen [aut] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: RELEASE_3_15 git_last_commit: 7c94bd8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/topdownr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topdownr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topdownr_1.18.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependsOnMe: topdownrdata dependencyCount: 83 Package: topGO Version: 2.48.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: 1b86ee5ca2c9b32378f01529a30cd2fb NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa git_url: https://git.bioconductor.org/packages/topGO git_branch: RELEASE_3_15 git_last_commit: a47f007 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/topGO_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topGO_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topGO_2.48.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, GRaNIE, ideal, moanin, tRanslatome, ccTutorial, maEndToEnd importsMe: APL, BioMM, cellity, FoldGO, GOSim, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: FGNet, geva, IntramiRExploreR, miRNAtap, pareg, Ringo dependencyCount: 51 Package: ToxicoGx Version: 2.0.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma, jsonlite Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable, markdown License: MIT + file LICENSE MD5sum: 32723577a04e3d2938bfc1a909de93a9 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: RELEASE_3_15 git_last_commit: 6150d7f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ToxicoGx_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ToxicoGx_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ToxicoGx_2.0.0.tgz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 130 Package: TPP Version: 3.24.0 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: ec48430f9175382053777007fc5e29d4 NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_15 git_last_commit: de49603 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TPP_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TPP_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TPP_3.24.0.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R suggestsMe: Rtpca dependencyCount: 94 Package: TPP2D Version: 1.12.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 24b8b23a8455653568bfdc197a226159 NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: RELEASE_3_15 git_last_commit: 80a0003 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TPP2D_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TPP2D_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TPP2D_1.12.0.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 64 Package: tracktables Version: 1.30.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: b215293d1afe9540b2cae627ed428071 NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: RELEASE_3_15 git_last_commit: 8176958 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tracktables_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tracktables_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tracktables_1.30.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 42 Package: trackViewer Version: 1.32.1 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid, Rcpp Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph, utils, rhdf5 LinkingTo: Rcpp Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown License: GPL (>= 2) MD5sum: 4b2fa9244d72c3fc89c57c702d78c805 NeedsCompilation: yes Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_15 git_last_commit: 7878202 git_last_commit_date: 2022-05-04 Date/Publication: 2022-05-15 source.ver: src/contrib/trackViewer_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/trackViewer_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/trackViewer_1.32.1.tgz vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html, vignettes/trackViewer/inst/doc/dandelionPlot.html, vignettes/trackViewer/inst/doc/lollipopPlot.html, vignettes/trackViewer/inst/doc/plotInteractionData.html, vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette: change the track styles, trackViewer Vignette: dandelionPlot, trackViewer Vignette: lollipopPlot, trackViewer Vignette: plot interaction data, trackViewer Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R, vignettes/trackViewer/inst/doc/dandelionPlot.R, vignettes/trackViewer/inst/doc/lollipopPlot.R, vignettes/trackViewer/inst/doc/plotInteractionData.R, vignettes/trackViewer/inst/doc/trackViewer.R importsMe: NADfinder, geneHapR suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 154 Package: tradeSeq Version: 1.10.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment License: MIT + file LICENSE MD5sum: 030669cd3000d0805f7991c788f25144 NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: RELEASE_3_15 git_last_commit: e20a2a2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tradeSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tradeSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tradeSeq_1.10.0.tgz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R dependsOnMe: OSCA.advanced dependencyCount: 74 Package: TrajectoryGeometry Version: 1.4.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 604eb4e761db4b3786f7a14964ba6c2b NeedsCompilation: no Title: This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns Description: Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: RELEASE_3_15 git_last_commit: 1ec0987 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TrajectoryGeometry_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TrajectoryGeometry_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TrajectoryGeometry_1.4.0.tgz vignettes: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R dependencyCount: 52 Package: TrajectoryUtils Version: 1.4.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 6a7ec44b6e44828bd779e1181fe9d1cf NeedsCompilation: no Title: Single-Cell Trajectory Analysis Utilities Description: Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. biocViews: GeneExpression, SingleCell Author: Aaron Lun [aut, cre], Kelly Street [aut] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/TrajectoryUtils VignetteBuilder: knitr BugReports: https://github.com/LTLA/TrajectoryUtils/issues git_url: https://git.bioconductor.org/packages/TrajectoryUtils git_branch: RELEASE_3_15 git_last_commit: d864cfb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TrajectoryUtils_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TrajectoryUtils_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TrajectoryUtils_1.4.0.tgz vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html vignetteTitles: Trajectory utilities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R dependsOnMe: slingshot, TSCAN importsMe: condiments, singleCellTK, tradeSeq dependencyCount: 30 Package: transcriptogramer Version: 1.18.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 533fd5c93b22fa5385a2ace972280531 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: RELEASE_3_15 git_last_commit: cf80925 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/transcriptogramer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transcriptogramer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transcriptogramer_1.18.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 105 Package: transcriptR Version: 1.24.0 Depends: R (>= 3.5.0), methods Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: af3d0258939993e8274e6ee05a3d9452 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: RELEASE_3_15 git_last_commit: d4ab47e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/transcriptR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transcriptR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transcriptR_1.24.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 152 Package: transformGamPoi Version: 1.2.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown License: GPL-3 MD5sum: e7bff785cea5d5e819fa2c846649e96f NeedsCompilation: yes Title: Variance Stabilizing Transformation for Gamma-Poisson Models Description: Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals). biocViews: SingleCell, Normalization, Preprocessing, Regression Author: Constantin Ahlmann-Eltze [aut, cre] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/transformGamPoi VignetteBuilder: knitr BugReports: https://github.com/const-ae/transformGamPoi/issues git_url: https://git.bioconductor.org/packages/transformGamPoi git_branch: RELEASE_3_15 git_last_commit: d3aa071 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/transformGamPoi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transformGamPoi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transformGamPoi_1.2.0.tgz vignettes: vignettes/transformGamPoi/inst/doc/transformGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transformGamPoi/inst/doc/transformGamPoi.R dependencyCount: 36 Package: transite Version: 1.14.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), ggseqlogo (>= 0.1), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: d6e6d55d8aeb4adfe3888ea04ddb06df NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (), Anna Gattinger [aut] (), Michael Yaffe [ths, cph] (), Ian Cannell [ths] () Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: RELEASE_3_15 git_last_commit: 125e0cb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/transite_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transite_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transite_1.14.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 58 Package: tRanslatome Version: 1.34.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 Archs: x64 MD5sum: bafd11121f02a0a8470f5198aa372513 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_15 git_last_commit: 50053cc git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tRanslatome_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRanslatome_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRanslatome_1.34.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 118 Package: transomics2cytoscape Version: 1.6.1 Imports: RCy3, KEGGREST, dplyr, purrr, tibble Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 76f70cc11f98c77a0ff692477feb508c NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (), Katsuyuki Yugi [aut] () Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.9.1 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_15 git_last_commit: 71df1bc git_last_commit_date: 2022-04-28 Date/Publication: 2022-04-29 source.ver: src/contrib/transomics2cytoscape_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/transomics2cytoscape_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/transomics2cytoscape_1.6.1.tgz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 63 Package: TransView Version: 1.40.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.15.3) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 MD5sum: a38e0a219325c6d0bef6052c6289ab73 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/TransView git_branch: RELEASE_3_15 git_last_commit: 1fc933d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TransView_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TransView_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TransView_1.40.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R dependencyCount: 21 Package: TraRe Version: 1.4.0 Depends: R (>= 4.1) Imports: hash, ggplot2, stats, methods, igraph, utils, glmnet, vbsr, grDevices, gplots, gtools, pvclust, R.utils, dqrng, SummarizedExperiment, BiocParallel, matrixStats Suggests: knitr, rmarkdown, BiocGenerics, RUnit, BiocStyle License: MIT + file LICENSE MD5sum: 4e6eee798f597cea921befc6b19a24df NeedsCompilation: no Title: Transcriptional Rewiring Description: TraRe (Transcriptional Rewiring) is an R package which contains the necessary tools to carry out several functions. Identification of module-based gene regulatory networks (GRN); score-based classification of these modules via a rewiring test; visualization of rewired modules to analyze condition-based GRN deregulation and drop out genes recovering via cliques methodology. For each tool, an html report can be generated containing useful information about the generated GRN and statistical data about the performed tests. These tools have been developed considering sequenced data (RNA-Seq). biocViews: GeneRegulation, RNASeq, GraphAndNetwork, Bayesian, GeneTarget, Classification Author: Jesus De La Fuente Cedeño [aut, cre, cph] (), Mikel Hernaez [aut, cph, ths] (), Charles Blatti [aut, cph] () Maintainer: Jesus De La Fuente Cedeño URL: https://github.com/ubioinformat/TraRe/tree/master VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TraRe git_branch: RELEASE_3_15 git_last_commit: 22e1e29 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TraRe_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TraRe_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TraRe_1.4.0.tgz vignettes: vignettes/TraRe/inst/doc/TraRe.html vignetteTitles: TraRe: Identification of conditions dependant Gene Regulatory Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TraRe/inst/doc/TraRe.R dependencyCount: 82 Package: traseR Version: 1.26.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL Archs: x64 MD5sum: 5725793db61abaa20365aef202942c8a NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: RELEASE_3_15 git_last_commit: 37dfd28 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/traseR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/traseR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/traseR_1.26.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 47 Package: Travel Version: 1.4.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, inline, parallel License: GPL-3 MD5sum: 47260d715d1de5161c5cb4c3270dbdca NeedsCompilation: yes Title: An utility to create an ALTREP object with a virtual pointer Description: Creates a virtual pointer for R's ALTREP object which does not have the data allocates in memory. The pointer is made by the file mapping of a virtual file so it behaves exactly the same as a regular pointer. All the requests to access the pointer will be sent to the underlying file system and eventually handled by a customized data-reading function. The main purpose of the package is to reduce the memory consumption when using R's vector to represent a large data. The use cases of the package include on-disk data representation, compressed vector(e.g. RLE) and etc. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang URL: https://github.com/Jiefei-Wang/Travel SystemRequirements: C++11 Windows: Dokan Linux&Mac: fuse, pkg-config VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/Travel/issues git_url: https://git.bioconductor.org/packages/Travel git_branch: RELEASE_3_15 git_last_commit: afe6340 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Travel_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Travel_1.4.0.zip vignettes: vignettes/Travel/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Travel/inst/doc/vignette.R dependencyCount: 3 Package: traviz Version: 1.2.0 Depends: R (>= 4.0) Imports: ggplot2, viridis, mgcv, SingleCellExperiment, slingshot, princurve, Biobase, methods, RColorBrewer, SummarizedExperiment, grDevices, graphics, rgl Suggests: scater, dplyr, testthat (>= 3.0.0), covr, S4Vectors, rmarkdown, knitr License: MIT + file LICENSE MD5sum: 92aa77147796b49197114866fefe47cc NeedsCompilation: no Title: Trajectory functions for visualization and interpretation. Description: traviz provides a suite of functions to plot trajectory related objects from Bioconductor packages. It allows plotting trajectories in reduced dimension, as well as averge gene expression smoothers as a function of pseudotime. Asides from general utility functions, traviz also allows plotting trajectories estimated by Slingshot, as well as smoothers estimated by tradeSeq. Furthermore, it allows for visualization of Slingshot trajectories using ggplot2. biocViews: GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, ctb], Kelly Street [aut, ctb], Koen Van den Berge [aut, cre] Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/traviz git_branch: RELEASE_3_15 git_last_commit: 8b6c513 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/traviz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/traviz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/traviz_1.2.0.tgz vignettes: vignettes/traviz/inst/doc/slingshot.html, vignettes/traviz/inst/doc/traviz.html vignetteTitles: ggplot2 + slingshot, traviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traviz/inst/doc/slingshot.R, vignettes/traviz/inst/doc/traviz.R dependencyCount: 75 Package: TreeAndLeaf Version: 1.8.0 Depends: R(>= 4.0) Imports: RedeR(>= 1.40.4), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 Archs: x64 MD5sum: 38bb7c8d35e1dbf9b7402b7795913d45 NeedsCompilation: no Title: Displaying binary trees with focus on dendrogram leaves Description: The TreeAndLeaf package combines unrooted and force-directed graph algorithms in order to layout binary trees, aiming to represent multiple layers of information onto dendrogram leaves. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro Maintainer: Milena A. Cardoso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: RELEASE_3_15 git_last_commit: d7e1a4c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TreeAndLeaf_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TreeAndLeaf_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TreeAndLeaf_1.8.0.tgz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an graph layout to dendrograms. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R suggestsMe: RedeR dependencyCount: 18 Package: treeio Version: 1.20.2 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.3.9), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: a1fb5efed1c6d2e980e6c5925db07209 NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/treeio (devel), https://docs.ropensci.org/treeio/ (docs), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), https://doi.org/10.1093/molbev/msz240 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_15 git_last_commit: ed457d6 git_last_commit_date: 2022-08-13 Date/Publication: 2022-08-14 source.ver: src/contrib/treeio_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/treeio_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/treeio_1.20.2.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree, MicrobiotaProcess, TreeSummarizedExperiment, EvoPhylo, ggmotif, shinyTempSignal, SurvivalPath suggestsMe: enrichplot, ggtreeExtra, rfaRm, idiogramFISH, nosoi dependencyCount: 36 Package: treekoR Version: 1.4.0 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 MD5sum: a45a0cb779e650986a45f198fe69e01f NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations Description: treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results. biocViews: Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization Author: Adam Chan [aut, cre], Ellis Patrick [ctb] Maintainer: Adam Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: RELEASE_3_15 git_last_commit: c20af91 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/treekoR_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/treekoR_1.4.0.tgz vignettes: vignettes/treekoR/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treekoR/inst/doc/vignette.R dependencyCount: 232 Package: TreeSummarizedExperiment Version: 2.4.0 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18), Biostrings Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment, BiocParallel, IRanges, treeio Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 6d66b0eccbb7a1a09ae08518d88a17e4 NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (), Felix G.M. Ernst [ctb] () Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: RELEASE_3_15 git_last_commit: e0dbf0c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TreeSummarizedExperiment_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TreeSummarizedExperiment_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TreeSummarizedExperiment_2.4.0.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R dependsOnMe: ExperimentSubset, mia, miaSim, miaViz, curatedMetagenomicData, microbiomeDataSets importsMe: benchdamic, CBEA suggestsMe: philr dependencyCount: 65 Package: TREG Version: 1.0.1 Depends: R (>= 4.2), SummarizedExperiment Imports: Matrix, purrr, rafalib Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap, sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0), tibble, tidyr, SingleCellExperiment License: Artistic-2.0 MD5sum: 160be7634084c76d4596c7a978707c66 NeedsCompilation: no Title: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Description: RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, Transcription, Sequencing Author: Louise Huuki-Myers [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/TREG, http://research.libd.org/TREG/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/TREG git_url: https://git.bioconductor.org/packages/TREG git_branch: RELEASE_3_15 git_last_commit: 86d92c8 git_last_commit_date: 2022-05-04 Date/Publication: 2022-05-15 source.ver: src/contrib/TREG_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TREG_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/TREG_1.0.1.tgz vignettes: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html vignetteTitles: How to find Total RNA Expression Genes (TREGs) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R dependencyCount: 30 Package: trena Version: 1.18.1 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, xgboost, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi, WGCNA Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, formatR, markdown, BiocParallel, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Athaliana.TAIR.TAIR9 License: GPL-3 MD5sum: e6ee43fe5773e67daace321b2b9a1ba8 NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon URL: https://pricelab.github.io/trena/ VignetteBuilder: knitr, rmarkdown, formatR, markdown BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_15 git_last_commit: 57161c5 git_last_commit_date: 2022-08-22 Date/Publication: 2022-08-23 source.ver: src/contrib/trena_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/trena_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/trena_1.18.1.tgz vignettes: vignettes/trena/inst/doc/caseStudyFour.html, vignettes/trena/inst/doc/caseStudyOne.html, vignettes/trena/inst/doc/caseStudyThree.html, vignettes/trena/inst/doc/caseStudyTwo.html, vignettes/trena/inst/doc/overview.html, vignettes/trena/inst/doc/simple.html, vignettes/trena/inst/doc/tiny.html, vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: "Case Study Four: a novel regulator of GATA2 in erythropoieis?", "Case Study One: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Three: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Two reproduces known regulation of NFE2 by GATA1 in erytrhop RNA-seq", "TRENA: computational prediction of gene regulation", "Explore output controls", "Tiny Vignette Example", A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/overview.R, vignettes/trena/inst/doc/simple.R, vignettes/trena/inst/doc/tiny.R, vignettes/trena/inst/doc/TReNA_Vignette.R dependencyCount: 161 Package: Trendy Version: 1.18.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: 46e1a264aba67bdb9359fa3ae66f1a43 NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_15 git_last_commit: 0e01eec git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Trendy_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Trendy_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Trendy_1.18.0.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 84 Package: TRESS Version: 1.2.0 Depends: R (>= 4.1.0), parallel, S4Vectors Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi Suggests: knitr, rmarkdown,BiocStyle License: GPL-3 + file LICENSE MD5sum: 0bc391c13b75334139ae0e059f1f1326 NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detection of transcriptome wide m6A methylation regions 2. detection of transcriptome wide differential m6A methylation regions. biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: RELEASE_3_15 git_last_commit: d75a72a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TRESS_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TRESS_1.2.0.tgz vignettes: vignettes/TRESS/inst/doc/TRESS.html vignetteTitles: The TRESS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRESS/inst/doc/TRESS.R dependencyCount: 97 Package: tricycle Version: 1.4.0 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater, GenomicRanges, IRanges, S4Vectors, scattermore, dplyr, RColorBrewer, grDevices, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats, cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 630a5d33d1b5a8e5533711596163dbee NeedsCompilation: no Title: tricycle: Transferable Representation and Inference of cell cycle Description: The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference. biocViews: SingleCell, Software, Transcriptomics, RNASeq, Transcription, BiologicalQuestion, DimensionReduction, ImmunoOncology Author: Shijie Zheng [aut, cre] Maintainer: Shijie Zheng URL: https://github.com/hansenlab/tricycle VignetteBuilder: knitr BugReports: https://github.com/hansenlab/tricycle/issues git_url: https://git.bioconductor.org/packages/tricycle git_branch: RELEASE_3_15 git_last_commit: 52c980e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tricycle_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tricycle_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tricycle_1.4.0.tgz vignettes: vignettes/tricycle/inst/doc/tricycle.html vignetteTitles: tricycle: Transferable Representation and Inference of Cell Cycle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tricycle/inst/doc/tricycle.R dependencyCount: 113 Package: trigger Version: 1.42.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 634b63c9963d3cf98fedbe82da880c0d NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: RELEASE_3_15 git_last_commit: 5448d40 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/trigger_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trigger_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trigger_1.42.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R dependencyCount: 96 Package: trio Version: 3.34.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: f41bd4eb4bbf6b99d978023b89575631 NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: RELEASE_3_15 git_last_commit: 56df07f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/trio_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trio_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trio_3.34.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 18 Package: triplex Version: 1.36.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: bee626a6f0fe918be7a745bf3f014dd8 NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: RELEASE_3_15 git_last_commit: f204d24 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/triplex_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/triplex_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/triplex_1.36.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 20 Package: tripr Version: 1.2.0 Depends: shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, pryr, config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices, graphics, stats, utils Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager, Biostrings, xtable, rlist, motifStack, knitr, rmarkdown, testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis Enhances: parallel License: MIT + file LICENSE MD5sum: e3a2a4d309b59ba20132f9107eb5b434 NeedsCompilation: no Title: T-cell Receptor/Immunoglobulin Profiler (TRIP) Description: TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly. biocViews: BatchEffect, MultipleComparison, GeneExpression, ImmunoOncology, TargetedResequencing Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos Pechlivanis [aut], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [cre] Maintainer: Iason Ofeidis URL: https://github.com/BiodataAnalysisGroup/tripr VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues git_url: https://git.bioconductor.org/packages/tripr git_branch: RELEASE_3_15 git_last_commit: 5ed7bca git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tripr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tripr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tripr_1.2.0.tgz vignettes: vignettes/tripr/inst/doc/tripr_guide.html vignetteTitles: tripr User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tripr/inst/doc/tripr_guide.R dependencyCount: 118 Package: tRNA Version: 1.14.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9075354cc5ae0bd8cf904d9817d1e286 NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] () Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: RELEASE_3_15 git_last_commit: 1949025 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tRNA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNA_1.14.0.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 54 Package: tRNAdbImport Version: 1.14.0 Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, methods, httr, IRanges, utils Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: 801e90878abcdc52d6c39166d233e6d2 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: RELEASE_3_15 git_last_commit: 82aea62 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tRNAdbImport_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNAdbImport_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNAdbImport_1.14.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 63 Package: tRNAscanImport Version: 1.16.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: ed86941f119e0cc2933448126d7d4522 NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: RELEASE_3_15 git_last_commit: c9f0257 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tRNAscanImport_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNAscanImport_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNAscanImport_1.16.0.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 79 Package: TRONCO Version: 2.28.0 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 MD5sum: 9879abb7edcf24503e9d99c211d02c63 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De Sano [aut] (), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] () Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: RELEASE_3_15 git_last_commit: 962edc7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TRONCO_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TRONCO_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TRONCO_2.28.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R dependencyCount: 45 Package: TSCAN Version: 1.34.0 Depends: SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, DelayedArray, S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 69936fe192a9d444ec10cbbd019d44a4 NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: RELEASE_3_15 git_last_commit: f9c8b02 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TSCAN_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TSCAN_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TSCAN_1.34.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R dependsOnMe: OSCA.advanced, OSCA.multisample importsMe: ctgGEM, FEAST, singleCellTK, DIscBIO suggestsMe: condiments dependencyCount: 87 Package: tscR Version: 1.8.0 Depends: R (>= 4.1.0), dplyr Imports: gridExtra, methods, dtw, class, kmlShape, graphics, cluster, RColorBrewer, grDevices, knitr, rmarkdown, prettydoc, grid, ggplot2, latex2exp, stats, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors Suggests: testthat License: Artistic-2.0 MD5sum: e5561fc2032a0afe11785b5ef9fea429 NeedsCompilation: yes Title: A time series clustering package combining slope and Frechet distances Description: Clustering for time series data using slope distance and/or shape distance. biocViews: GeneExpression, Clustering, DNAMethylation, Microarray Author: Fernando Pérez-Sanz [aut, cre], Miriam Riquelme-Pérez [aut] Maintainer: Fernando Pérez-Sanz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tscR git_branch: RELEASE_3_15 git_last_commit: f732e9e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tscR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tscR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tscR_1.8.0.tgz vignettes: vignettes/tscR/inst/doc/tscR.html vignetteTitles: tscR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tscR/inst/doc/tscR.R dependencyCount: 92 Package: tspair Version: 1.53.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 MD5sum: 2ae2033a88f85b75e2f72ffdc6be0956 NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/tspair git_branch: master git_last_commit: 365e3cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tspair_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tspair_1.53.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tspair_1.53.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependencyCount: 6 Package: ttgsea Version: 1.4.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 4a906f8eb647dbef7d98678c5c56dd6c NeedsCompilation: no Title: Tokenizing Text of Gene Set Enrichment Analysis Description: Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: RELEASE_3_15 git_last_commit: 80772d0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ttgsea_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ttgsea_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ttgsea_1.4.0.tgz vignettes: vignettes/ttgsea/inst/doc/ttgsea.html vignetteTitles: ttgsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R importsMe: DeepPINCS, GenProSeq dependencyCount: 124 Package: TTMap Version: 1.18.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 Archs: x64 MD5sum: b0106a59a1a1ecb03cc8172e4b8f26ac NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: RELEASE_3_15 git_last_commit: 54db755 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TTMap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TTMap_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TTMap_1.18.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 45 Package: TurboNorm Version: 1.44.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL MD5sum: 897a0830aca17fbb9070c4fa7d1e0d91 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: RELEASE_3_15 git_last_commit: 310a4ff git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TurboNorm_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TurboNorm_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TurboNorm_1.44.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 17 Package: TVTB Version: 1.22.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 25c357e1c9f1c9ac0f4f8e80ef410592 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_15 git_last_commit: 19e4f04 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TVTB_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TVTB_1.22.0.tgz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 155 Package: tweeDEseq Version: 1.42.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) MD5sum: 36ec45aea0a7d8b0db78607e59681d91 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_15 git_last_commit: d74d181 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tweeDEseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tweeDEseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tweeDEseq_1.42.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 23 Package: twilight Version: 1.72.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: x64 MD5sum: a077e6321b23737aadaa2ad67bb72a8b NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: RELEASE_3_15 git_last_commit: b0a85f0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/twilight_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/twilight_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/twilight_1.72.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 8 Package: twoddpcr Version: 1.20.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: a26620cdda6ce4be6dbf01caf68ef992 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: RELEASE_3_15 git_last_commit: 40a31e1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/twoddpcr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/twoddpcr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/twoddpcr_1.20.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 65 Package: txcutr Version: 1.2.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, GenomicFeatures, IRanges, GenomicRanges, BiocGenerics, Biostrings, S4Vectors, rtracklayer, BiocParallel, stats, methods, utils Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown, testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 Archs: x64 MD5sum: 5654e24651c78d3456507f60e5f0f0a5 NeedsCompilation: no Title: Transcriptome CUTteR Description: Various mRNA sequencing library preparation methods generate sequencing reads specifically from the transcript ends. Analyses that focus on quantification of isoform usage from such data can be aided by using truncated versions of transcriptome annotations, both at the alignment or pseudo-alignment stage, as well as in downstream analysis. This package implements some convenience methods for readily generating such truncated annotations and their corresponding sequences. biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics Author: Mervin Fansler [aut, cre] () Maintainer: Mervin Fansler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/txcutr git_branch: RELEASE_3_15 git_last_commit: 52c0f97 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/txcutr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/txcutr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/txcutr_1.2.0.tgz vignettes: vignettes/txcutr/inst/doc/intro.html vignetteTitles: Introduction to txcutr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txcutr/inst/doc/intro.R dependencyCount: 97 Package: tximeta Version: 1.14.1 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, fishpond, edgeR, limma, devtools License: GPL-2 MD5sum: 6fd3c840deb671c43a09194f11ab222d NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/mikelove/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_15 git_last_commit: 6e91f08 git_last_commit_date: 2022-07-16 Date/Publication: 2022-07-17 source.ver: src/contrib/tximeta_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tximeta_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tximeta_1.14.1.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: Transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R dependsOnMe: rnaseqGene importsMe: IsoformSwitchAnalyzeR suggestsMe: DESeq2, fishpond, fluentGenomics dependencyCount: 123 Package: tximport Version: 1.24.0 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, fishpond License: GPL (>=2) MD5sum: 9da276e7a5c1c7f6341eccba54d7a307 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_15 git_last_commit: 58524f3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/tximport_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tximport_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tximport_1.24.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, tximeta, seeker suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TypeInfo Version: 1.62.0 Depends: methods Suggests: Biobase License: BSD_2_clause MD5sum: 2ad0a9f18a29f42126e1b75581ff1138 NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: RELEASE_3_15 git_last_commit: acdc55e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TypeInfo_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TypeInfo_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TypeInfo_1.62.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: UCell Version: 2.0.1 Depends: R(>= 4.1.0) Imports: methods, data.table(>= 1.13.6), Matrix, BiocParallel, SingleCellExperiment, SummarizedExperiment Suggests: Seurat, scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 1946194bef427c7030cb00aed30d54ff NeedsCompilation: no Title: Rank-based signature enrichment analysis for single-cell data Description: UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, GeneExpression, CellBasedAssays Author: Massimo Andreatta [aut, cre] (), Santiago Carmona [aut] () Maintainer: Massimo Andreatta URL: https://github.com/carmonalab/UCell VignetteBuilder: knitr BugReports: https://github.com/carmonalab/UCell/issues git_url: https://git.bioconductor.org/packages/UCell git_branch: RELEASE_3_15 git_last_commit: 2b5cd45 git_last_commit_date: 2022-06-21 Date/Publication: 2022-06-21 source.ver: src/contrib/UCell_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/UCell_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/UCell_2.0.1.tgz vignettes: vignettes/UCell/inst/doc/UCell_vignette_basic.html vignetteTitles: Gene signature scoring with UCell hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UCell/inst/doc/UCell_vignette_basic.R importsMe: escape dependencyCount: 36 Package: Ularcirc Version: 1.14.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments, Organism.dplyr, plotgardener, R.utils, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 1caff462ed4918e36dcd129789d59849 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: RELEASE_3_15 git_last_commit: cb2b7d3 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Ularcirc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Ularcirc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Ularcirc_1.14.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 154 Package: UMI4Cats Version: 1.6.0 Depends: R (>= 4.0.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate, TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: c0ad8e42de3594c55ed5139ff9230432 NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: RELEASE_3_15 git_last_commit: 056cb00 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/UMI4Cats_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/UMI4Cats_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/UMI4Cats_1.6.0.tgz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 158 Package: uncoverappLib Version: 1.6.0 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 9e9bd922e42f70ebddf5a6300375d0fe NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: RELEASE_3_15 git_last_commit: 500443a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/uncoverappLib_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/uncoverappLib_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/uncoverappLib_1.6.0.tgz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 187 Package: UNDO Version: 1.38.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 67c833fe4452f5f272f29c76b19211f6 NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: RELEASE_3_15 git_last_commit: ce2ef1c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/UNDO_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/UNDO_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/UNDO_1.38.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 10 Package: unifiedWMWqPCR Version: 1.32.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: b6041dadfb7ecdb18495c766c0598fdf NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_15 git_last_commit: 62b47a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/unifiedWMWqPCR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/unifiedWMWqPCR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/unifiedWMWqPCR_1.32.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 21 Package: UniProt.ws Version: 2.36.5 Depends: methods, utils, RSQLite, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, cellxgenedp, jsonlite, httr, httpcache, progress Suggests: RUnit, BiocStyle, knitr License: Artistic License 2.0 MD5sum: 563175f4c6b42677bd0d1d15a14f0d27 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the UniProt web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_15 git_last_commit: 99d4c85 git_last_commit_date: 2022-08-19 Date/Publication: 2022-08-21 source.ver: src/contrib/UniProt.ws_2.36.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/UniProt.ws_2.36.5.zip mac.binary.ver: bin/macosx/contrib/4.2/UniProt.ws_2.36.5.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo, drugTargetInteractions suggestsMe: cleaver, qPLEXanalyzer dependencyCount: 89 Package: Uniquorn Version: 2.16.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 830ca43a4b7691eead1c32d5a81918c9 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: This packages enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology. RNA-seq data is very likely to work as well but hasn't been rigiously tested yet. Panel-seq will require manual adjustment of thresholds biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_15 git_last_commit: c389295 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Uniquorn_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Uniquorn_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Uniquorn_2.16.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R dependencyCount: 106 Package: universalmotif Version: 1.14.1 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio Enhances: PWMEnrich, rGADEM License: GPL-3 MD5sum: 3f64427edea896d8e9d2b47703e74bdb NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (), Spencer Nystrom [ctb] () Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: RELEASE_3_15 git_last_commit: 0221c4f git_last_commit_date: 2022-05-30 Date/Publication: 2022-05-31 source.ver: src/contrib/universalmotif_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/universalmotif_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/universalmotif_1.14.1.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: circRNAprofiler, memes, ggmotif suggestsMe: spiky dependencyCount: 52 Package: updateObject Version: 1.0.0 Depends: R (>= 4.2.0), methods, BiocGenerics, S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 4107afb432e264898f37e350c752bf4e NeedsCompilation: no Title: Find/fix old serialized S4 instances Description: A set of tools built around updateObject() to work with old serialized S4 instances. The package is primarily useful to package maintainers who want to update the serialized S4 instances included in their package. This is still work-in-progress. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues git_url: https://git.bioconductor.org/packages/updateObject git_branch: RELEASE_3_15 git_last_commit: 4fe6a9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/updateObject_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/updateObject_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/updateObject_1.0.0.tgz vignettes: vignettes/updateObject/inst/doc/updateObject.html vignetteTitles: A quick introduction to the updateObject package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/updateObject/inst/doc/updateObject.R dependencyCount: 8 Package: uSORT Version: 1.22.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: a83d673991bf1ba48bfb8d5bc1fec24f NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: RELEASE_3_15 git_last_commit: ae9b0b9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/uSORT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/uSORT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/uSORT_1.22.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 98 Package: VAExprs Version: 1.2.1 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: b15ffa0d53f259abcbd7b76817be5e67 NeedsCompilation: no Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones. biocViews: Software, GeneExpression, SingleCell Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: RELEASE_3_15 git_last_commit: 528bf0a git_last_commit_date: 2022-05-18 Date/Publication: 2022-05-19 source.ver: src/contrib/VAExprs_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/VAExprs_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/VAExprs_1.2.1.tgz vignettes: vignettes/VAExprs/inst/doc/VAExprs.html vignetteTitles: VAExprs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R dependencyCount: 189 Package: VanillaICE Version: 1.58.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 5e680c6f829c2ab5737f08a1b49444f6 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_15 git_last_commit: d648593 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VanillaICE_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VanillaICE_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VanillaICE_1.58.0.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 83 Package: VarCon Version: 1.4.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: c88eb053a1c79f95e91c4d7db4bcb2a5 NeedsCompilation: no Title: VarCon: an R package for retrieving neighboring nucleotides of an SNV Description: VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] Maintainer: Johannes Ptok VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: RELEASE_3_15 git_last_commit: c948472 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VarCon_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VarCon_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VarCon_1.4.0.tgz vignettes: vignettes/VarCon/inst/doc/VarCon.html vignetteTitles: Analysing SNVs with VarCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VarCon/inst/doc/VarCon.R dependencyCount: 98 Package: variancePartition Version: 1.26.0 Depends: R (>= 4.0.0), ggplot2, limma, BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix, iterators, foreach, doParallel, gplots, RhpcBLASctl, progress, reshape2, aod, scales, Rdpack, rlang, lme4 (>= 1.1-10), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL-2 Archs: x64 MD5sum: 037769ef54f934f8813f64a76be58b62 NeedsCompilation: no Title: Quantify and interpret divers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] Maintainer: Gabriel E. Hoffman URL: http://bioconductor.org/packages/variancePartition, https://DiseaseNeuroGenomics.github.io/variancePartition VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_15 git_last_commit: b173129 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/variancePartition_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/variancePartition_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/variancePartition_1.26.0.tgz vignettes: vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/FAQ.html, vignettes/variancePartition/inst/doc/theory_practice_random_effects.html vignetteTitles: 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Frequently asked questions, 3) Theory and practice of random effects and REML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/FAQ.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R importsMe: muscat dependencyCount: 106 Package: VariantAnnotation Version: 1.42.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 1.99.0) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 MD5sum: fbd24726967995e6e111bcc5cb69433d NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Bioconductor Package Maintainer [aut, cre], Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_15 git_last_commit: d112169 git_last_commit_date: 2022-05-12 Date/Publication: 2022-05-15 source.ver: src/contrib/VariantAnnotation_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantAnnotation_1.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantAnnotation_1.42.1.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, HelloRanges, HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector, PlasmaMutationDetector2 importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, CNVfilteR, CopyNumberPlots, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, epialleleR, fcScan, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, ldblock, lineagespot, MADSEQ, MMAPPR2, motifbreakR, MungeSumstats, musicatk, MutationalPatterns, ProteoDisco, scoreInvHap, SigsPack, SNPhood, svaRetro, TitanCNA, tLOH, TVTB, Uniquorn, VCFArray, YAPSA, COSMIC.67, SNPassoc suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CNVgears, CrispRVariants, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, omicsPrint, podkat, RVS, SeqArray, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD, updog dependencyCount: 98 Package: VariantExperiment Version: 1.10.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown License: GPL-3 MD5sum: e075e35d62cedc0efdf6f78ab2c4d822 NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: RELEASE_3_15 git_last_commit: facc20e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VariantExperiment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantExperiment_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantExperiment_1.10.0.tgz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html vignetteTitles: VariantExperiment-class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R dependencyCount: 80 Package: VariantFiltering Version: 1.32.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: x64 MD5sum: 26e3b2084570c84b54a57055df59fc1c NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: RELEASE_3_15 git_last_commit: e5baf3d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VariantFiltering_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantFiltering_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantFiltering_1.32.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 174 Package: VariantTools Version: 1.38.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3) License: Artistic-2.0 MD5sum: 217497641fc29ec4a2f2e417e7c3bc1b NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: RELEASE_3_15 git_last_commit: 517f59a git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VariantTools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantTools_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantTools_1.38.0.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie, MMAPPR2 suggestsMe: VariantToolsData dependencyCount: 99 Package: VaSP Version: 1.8.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) Archs: x64 MD5sum: 91644da5ef239800b0e37472312957b6 NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.) biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (), Qian Du [aut] (), Chi Zhang [aut] () Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues git_url: https://git.bioconductor.org/packages/VaSP git_branch: RELEASE_3_15 git_last_commit: 5ad0f55 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VaSP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VaSP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VaSP_1.8.0.tgz vignettes: vignettes/VaSP/inst/doc/VaSP.html vignetteTitles: user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VaSP/inst/doc/VaSP.R dependencyCount: 85 Package: vbmp Version: 1.64.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 6fc1c31a4cbcee9b248fe2a2624a946a NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: RELEASE_3_15 git_last_commit: a32e22e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vbmp_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vbmp_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vbmp_1.64.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.12.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 46e1ed92e432b4722dd715a1e7cf58f6 NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: RELEASE_3_15 git_last_commit: 4468f9b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VCFArray_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VCFArray_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VCFArray_1.12.0.tgz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 100 Package: VegaMC Version: 3.34.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: c5efef23f8d8ecb87d9ac2116d8af7cf NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: RELEASE_3_15 git_last_commit: 2099b12 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VegaMC_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VegaMC_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VegaMC_3.34.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 71 Package: velociraptor Version: 1.6.0 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran, scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot, GGally, patchwork, metR License: MIT + file LICENSE MD5sum: a234af5d40ad0ef5cb718424edfcb1a0 NeedsCompilation: no Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (), Aaron Lun [aut] (), Charlotte Soneson [aut] (), Michael Stadler [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues git_url: https://git.bioconductor.org/packages/velociraptor git_branch: RELEASE_3_15 git_last_commit: 3a7545b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/velociraptor_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/velociraptor_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/velociraptor_1.6.0.tgz vignettes: vignettes/velociraptor/inst/doc/velociraptor.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R dependsOnMe: OSCA.advanced dependencyCount: 58 Package: veloviz Version: 1.2.1 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: df9bec55057722d69d68b3e75480e0f0 NeedsCompilation: yes Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories Description: VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories. biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing, RNASeq, DimensionReduction Author: Lyla Atta [aut, cre] (), Jean Fan [aut] (), Arpan Sahoo [aut] () Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: RELEASE_3_15 git_last_commit: 80e12d4 git_last_commit_date: 2022-09-05 Date/Publication: 2022-09-06 source.ver: src/contrib/veloviz_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/veloviz_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/veloviz_1.2.1.tgz vignettes: vignettes/veloviz/inst/doc/vignette.html vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using VeloViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/veloviz/inst/doc/vignette.R dependencyCount: 18 Package: VennDetail Version: 1.12.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat, markdown License: GPL-2 MD5sum: 8af2e27fc871536ac3d0c2fc3128279b NeedsCompilation: no Title: A package for visualization and extract details Description: A set of functions to generate high-resolution Venn,Vennpie plot,extract and combine details of these subsets with user datasets in data frame is available. biocViews: DataRepresentation,GraphAndNetwork Author: Kai Guo, Brett McGregor Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: RELEASE_3_15 git_last_commit: 92637c1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VennDetail_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VennDetail_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VennDetail_1.12.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 49 Package: VERSO Version: 1.6.0 Depends: R (>= 4.1.0) Imports: ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE Archs: x64 MD5sum: 9b45aadb1ca2a409292c27b4cb14db9b NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] () Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: RELEASE_3_15 git_last_commit: 71e3d53 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VERSO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VERSO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VERSO_1.6.0.tgz vignettes: vignettes/VERSO/inst/doc/vignette.pdf vignetteTitles: VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/vignette.R dependencyCount: 16 Package: vidger Version: 1.16.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: d97dd4b08bfa9cf2d1156385d9238f8f NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: RELEASE_3_15 git_last_commit: 3787258 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vidger_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vidger_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vidger_1.16.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 126 Package: viper Version: 1.30.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 1f835da7c14e57cecbac18ed82bcfb08 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: RELEASE_3_15 git_last_commit: 4806c73 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/viper_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/viper_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/viper_1.30.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan, aracne.networks importsMe: diggit, RTN, diggitdata, dorothea suggestsMe: decoupleR, MethReg, MOMA dependencyCount: 22 Package: ViSEAGO Version: 1.10.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt, dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim, ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph, methods, plotly, processx, topGO, RColorBrewer, R.utils, scales, stats, UpSetR, utils Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager License: GPL-3 MD5sum: de5fecf2484964de2eecba89a37d4211 NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: RELEASE_3_15 git_last_commit: a722952 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ViSEAGO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ViSEAGO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ViSEAGO_1.10.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 155 Package: vissE Version: 1.4.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr License: GPL-3 MD5sum: 045d99bcf4d4e6f1b9723c0692fdf8ed NeedsCompilation: no Title: Visualising Set Enrichment Analysis Results Description: This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches. biocViews: Software, GeneExpression, GeneSetEnrichment, NetworkEnrichment, Network Author: Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/vissE VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/vissE/issues git_url: https://git.bioconductor.org/packages/vissE git_branch: RELEASE_3_15 git_last_commit: f897fcb git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vissE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vissE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vissE_1.4.0.tgz vignettes: vignettes/vissE/inst/doc/vissE.html vignetteTitles: vissE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vissE/inst/doc/vissE.R suggestsMe: msigdb dependencyCount: 160 Package: VplotR Version: 1.6.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 MD5sum: 95d3fdcf7d52654bde2998c700c6cdc6 NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: RELEASE_3_15 git_last_commit: e48df2f git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/VplotR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VplotR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VplotR_1.6.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 74 Package: vsn Version: 3.64.0 Depends: R (>= 4.0.0), methods, Biobase Imports: affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr, testthat License: Artistic-2.0 MD5sum: 5c2b98cd8b77eb02bb26ebd091b12308 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: RELEASE_3_15 git_last_commit: 1f09f20 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vsn_3.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vsn_3.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vsn_3.64.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: cellHTS2, webbioc, rnaseqGene importsMe: arrayQualityMetrics, bnem, DEP, Doscheda, imageHTS, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca, Ringo, tilingArray, ExpressionNormalizationWorkflow, RNAseqQC suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, scp, twilight, estrogen, wrMisc dependencyCount: 44 Package: vtpnet Version: 0.36.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 03f978e252562dc8e827f324e7748493 NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: RELEASE_3_15 git_last_commit: a5c78f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vtpnet_0.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vtpnet_0.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vtpnet_0.36.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 134 Package: vulcan Version: 1.18.0 Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq2, Biobase Suggests: vulcandata License: LGPL-3 Archs: x64 MD5sum: 563d31842ea8bcbcee4eeab92e0d79e9 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: RELEASE_3_15 git_last_commit: 554d536 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/vulcan_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vulcan_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vulcan_1.18.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R dependencyCount: 180 Package: waddR Version: 1.10.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache, BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: 58a42852515dbff7c7c74da714d067da NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: RELEASE_3_15 git_last_commit: bdeb879 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/waddR_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/waddR_1.10.0.tgz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 117 Package: wateRmelon Version: 2.2.0 Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BiocStyle, knitr, rmarkdown, IlluminaHumanMethylationEPICmanifest, irlba, FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore Enhances: minfi License: GPL-3 MD5sum: 0318f386f9636e5f838f08a69ced2c85 NeedsCompilation: no Title: Illumina 450 and EPIC methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb], Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_15 git_last_commit: 6ec49ef git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/wateRmelon_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wateRmelon_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wateRmelon_2.2.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html vignetteTitles: wateRmelon User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 169 Package: wavClusteR Version: 2.30.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: cf2f82de22f14d7e57ffce07dcec2cf3 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: RELEASE_3_15 git_last_commit: d2d52b1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/wavClusteR_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wavClusteR_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wavClusteR_2.30.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 145 Package: weaver Version: 1.62.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 65eb87500cc17da29ee71a007f944bae NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: RELEASE_3_15 git_last_commit: 3a217f8 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/weaver_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/weaver_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/weaver_1.62.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R dependencyCount: 4 Package: webbioc Version: 1.68.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: 1071abd8610f54f91c5a0140c659e1c1 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: RELEASE_3_15 git_last_commit: 998ce5c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/webbioc_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/webbioc_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/webbioc_1.68.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 86 Package: weitrix Version: 1.8.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: ebd275899c8d8ab4d45f418a83785b90 NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: RELEASE_3_15 git_last_commit: 42874c9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/weitrix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/weitrix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/weitrix_1.8.0.tgz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 81 Package: widgetTools Version: 1.74.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: a3b3a73c4c9766f441d6853bc741b256 NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: RELEASE_3_15 git_last_commit: 4f80048 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/widgetTools_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/widgetTools_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/widgetTools_1.74.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.20.0 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 MD5sum: 3ac0bfde16761afb4e1a68bc78527ead NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: RELEASE_3_15 git_last_commit: 71367a0 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/wiggleplotr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wiggleplotr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wiggleplotr_1.20.0.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R dependencyCount: 79 Package: wpm Version: 1.6.0 Depends: R (>= 4.1.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: c24cb757d6e479a3428b29929decf5b8 NeedsCompilation: no Title: Well Plate Maker Description: The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges URL: https://github.com/HelBor/wpm, https://bioconductor.org/packages/release/bioc/html/wpm.html VignetteBuilder: knitr BugReports: https://github.com/HelBor/wpm/issues git_url: https://git.bioconductor.org/packages/wpm git_branch: RELEASE_3_15 git_last_commit: 993c918 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/wpm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wpm_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wpm_1.6.0.tgz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 123 Package: wppi Version: 1.4.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: a57c6ed5228ef3ad653ed153f7c1525a NeedsCompilation: no Title: Weighting protein-protein interactions Description: Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Author: Ana Galhoz [cre, aut] (), Denes Turei [aut] (), Michael P. Menden [aut] (), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues git_url: https://git.bioconductor.org/packages/wppi git_branch: RELEASE_3_15 git_last_commit: 958873d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/wppi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wppi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wppi_1.4.0.tgz vignettes: vignettes/wppi/inst/doc/wppi_workflow.html vignetteTitles: WPPI workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R dependencyCount: 79 Package: Wrench Version: 1.14.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: 4bae2946fea5b9fd2b7db8c8d549ff99 NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: RELEASE_3_15 git_last_commit: 72d3634 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Wrench_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Wrench_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Wrench_1.14.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq suggestsMe: PLNmodels dependencyCount: 10 Package: xcms Version: 3.18.0 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0), MSnbase (>= 2.21.4) Imports: mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.25.1), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges, SummarizedExperiment, MsCoreUtils, MsFeatures Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat, pander, magrittr, rmarkdown, multtest, MALDIquant, pheatmap, Spectra (>= 1.1.17), MsBackendMgf, progress, signal Enhances: Rgraphviz, rgl, XML License: GPL (>= 2) + file LICENSE MD5sum: 760e6235573fcb9edf3ab2c0a65948cc NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith [ctb], Ralf Tautenhahn [ctb], Steffen Neumann [aut, cre] (), Paul Benton [ctb], Christopher Conley [ctb], Johannes Rainer [ctb] (), Michael Witting [ctb], William Kumler [ctb] () Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: RELEASE_3_15 git_last_commit: f205ce1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/xcms_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xcms_3.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xcms_3.18.0.tgz vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LCMS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/LC-MS-feature-grouping.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, ncGTW, proFIA, faahKO, PtH2O2lipids importsMe: CAMERA, cliqueMS, cosmiq, MobilityTransformR, Risa suggestsMe: CluMSID, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, MetabolomicsBasics, RAMClustR dependencyCount: 91 Package: xcore Version: 1.0.0 Depends: R (>= 4.2) Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>= 1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges (>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix (>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>= 1.18.0), stats, S4Vectors (>= 0.30.0), utils Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0), BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>= 1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16), ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>= 1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>= 2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis (>= 2.0.1), xcoredata License: GPL-2 MD5sum: 6b014a454d7c2dcf10b3db805ea2a2b6 NeedsCompilation: no Title: xcore expression regulators inference Description: xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes. biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression, Sequencing Author: Maciej Migdał [aut, cre] (), Bogumił Kaczkowski [aut] () Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: RELEASE_3_15 git_last_commit: 58c5cee git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/xcore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xcore_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xcore_1.0.0.tgz vignettes: vignettes/xcore/inst/doc/xcore_vignette.html vignetteTitles: xcore vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R suggestsMe: xcoredata dependencyCount: 58 Package: XDE Version: 2.42.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 Archs: x64 MD5sum: 5fc6e4a684e426c0ffa8bcd3f3571308 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: RELEASE_3_15 git_last_commit: 298e83e git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/XDE_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XDE_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XDE_2.42.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 62 Package: Xeva Version: 1.12.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 8878405b2aa984176e92406e801de89f NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: Contains set of functions to perform analysis of patient-derived xenograft (PDX) data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_15 git_last_commit: 8d7f881 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/Xeva_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Xeva_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Xeva_1.12.0.tgz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 154 Package: XINA Version: 1.14.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 282f2a2298f1282dfe78721bf351ba3c NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: RELEASE_3_15 git_last_commit: db3bda2 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/XINA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XINA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XINA_1.14.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 66 Package: xmapbridge Version: 1.54.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: ad1952e2883b34b8be2452832e1a64fd NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: RELEASE_3_15 git_last_commit: a316e23 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/xmapbridge_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xmapbridge_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xmapbridge_1.54.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: XNAString Version: 1.4.0 Depends: R (>= 4.1) Imports: utils, Biostrings, BSgenome, data.table, GenomicRanges, IRanges, methods, Rcpp, stringi, S4Vectors, future.apply, stringr, formattable, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, BSgenome.Hsapiens.UCSC.hg38, pander License: GPL-2 MD5sum: 7c861cdc5516f8d06d8c81e8ce37e7cb NeedsCompilation: yes Title: Efficient Manipulation of Modified Oligonucleotide Sequences Description: The XNAString package allows for description of base sequences and associated chemical modifications in a single object. XNAString is able to capture single stranded, as well as double stranded molecules. Chemical modifications are represented as independent strings associated with different features of the molecules (base sequence, sugar sequence, backbone sequence, modifications) and can be read or written to a HELM notation. It also enables secondary structure prediction using RNAfold from ViennaRNA. XNAString is designed to be efficient representation of nucleic-acid based therapeutics, therefore it stores information about target sequences and provides interface for matching and alignment functions from Biostrings package. biocViews: SequenceMatching, Alignment, Sequencing, Genetics Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut], Peter H. Hagedorn [aut] Maintainer: Marianna Plucinska VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XNAString git_branch: RELEASE_3_15 git_last_commit: 3acea03 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/XNAString_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XNAString_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XNAString_1.4.0.tgz vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html vignetteTitles: XNAString classes and functionalities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R dependencyCount: 79 Package: XVector Version: 0.36.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) Imports: methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 MD5sum: 666d200f401a7e3c6cb509425e4d68fe NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: RELEASE_3_15 git_last_commit: ff6f818 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/XVector_0.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XVector_0.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XVector_0.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation, simMP suggestsMe: IRanges, musicatk linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.22.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 Archs: x64 MD5sum: 12f7f03465dff9290f444030c84fdabc NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, Software Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: RELEASE_3_15 git_last_commit: e4b9623 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/yamss_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/yamss_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/yamss_1.22.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 47 Package: YAPSA Version: 1.22.0 Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 22f584728e5062ed3c12767c92dbd50e NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann [aut, cre], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut], Matthias Schlesner [aut] Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_15 git_last_commit: 55c2886 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/YAPSA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/YAPSA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/YAPSA_1.22.0.tgz vignettes: vignettes/YAPSA/inst/doc/index.html, vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html, vignettes/YAPSA/inst/doc/vignette_exomes.html, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html, vignettes/YAPSA/inst/doc/vignettes_Indel.html, vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: index.html, 3. Confidence Intervals, 6. Usage of YAPSA for WES data, 2. Signature-specific cutoffs, 4. Stratified Analysis of Mutational Signatures, 5. Indel signature analysis, 1. Usage of YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R, vignettes/YAPSA/inst/doc/vignette_exomes.R, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R, vignettes/YAPSA/inst/doc/vignettes_Indel.R, vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 195 Package: yarn Version: 1.22.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: 65ab801505f64e57ebad8855dcc3e731 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: RELEASE_3_15 git_last_commit: 0d94152 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/yarn_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/yarn_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/yarn_1.22.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependsOnMe: netZooR dependencyCount: 160 Package: zellkonverter Version: 1.6.5 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils, cli Suggests: covr, spelling, testthat, knitr, rmarkdown, BiocStyle, scRNAseq, HDF5Array, rhdf5, BiocFileCache License: MIT + file LICENSE MD5sum: 236439324da0089fdcf2beab8eab8eb9 NeedsCompilation: no Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (), Aaron Lun [aut] () Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues git_url: https://git.bioconductor.org/packages/zellkonverter git_branch: RELEASE_3_15 git_last_commit: ee2351a git_last_commit_date: 2022-09-13 Date/Publication: 2022-09-13 source.ver: src/contrib/zellkonverter_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/zellkonverter_1.6.5.zip mac.binary.ver: bin/macosx/contrib/4.2/zellkonverter_1.6.5.tgz vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html vignetteTitles: Converting to/from AnnData to SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R dependsOnMe: OSCA.intro importsMe: velociraptor suggestsMe: cellxgenedp, HDF5Array dependencyCount: 41 Package: zFPKM Version: 1.18.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 5263ec5f2fd3ad93c826b67a8cc67153 NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: RELEASE_3_15 git_last_commit: 8bd9d29 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/zFPKM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zFPKM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zFPKM_1.18.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R suggestsMe: DGEobj.utils dependencyCount: 62 Package: zinbwave Version: 1.18.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2 License: Artistic-2.0 MD5sum: 855d38463b4869976dcb00fff8a73a26 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: RELEASE_3_15 git_last_commit: 678c0f6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/zinbwave_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zinbwave_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zinbwave_1.18.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: benchdamic, clusterExperiment, scBFA, singleCellTK, digitalDLSorteR suggestsMe: MAST, splatter dependencyCount: 73 Package: zlibbioc Version: 1.42.0 License: Artistic-2.0 + file LICENSE MD5sum: 29fb2154b3d2fbe49ae8a56f674223d0 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/zlibbioc BugReports: https://github.com/Bioconductor/zlibbioc/issues git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: RELEASE_3_15 git_last_commit: aa074d7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/zlibbioc_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zlibbioc_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zlibbioc_1.42.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: SimRAD importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, FLAMES, MADSEQ, makecdfenv, NanoMethViz, oligo, ompBAM, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, ChemmineOB, csaw, diffHic, epialleleR, FLAMES, maftools, methylKit, NxtIRFcore, Rfastp, Rhtslib, scPipe, seqTools, ShortRead, jackalope dependencyCount: 0 Package: CountClust Version: 1.23.1 Depends: R (>= 3.4), ggplot2 (>= 2.1.0) Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) NeedsCompilation: no Title: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models Description: Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships. biocViews: ImmunoOncology, RNASeq, GeneExpression, Clustering, Sequencing, StatisticalMethod, Software, Visualization Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/CountClust VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/CountClust git_branch: master git_last_commit: 1b59fba git_last_commit_date: 2021-11-15 Date/Publication: 2022-01-28 win.binary.ver: bin/windows/contrib/4.2/CountClust_1.23.1.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Herper Version: 1.6.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA License: GPL-3 Archs: x64 NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (), Thomas Carroll [aut, cre] (), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: RELEASE_3_15 git_last_commit: f903ed1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 win.binary.ver: bin/windows/contrib/4.2/Herper_1.6.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScISI Version: 1.68.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang PackageStatus: Deprecated Package: SLGI Version: 1.56.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur PackageStatus: Deprecated Package: RmiR Version: 1.52.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero PackageStatus: Deprecated Package: ABAEnrichment Version: 1.26.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0), ABAData (>= 0.99.2), data.table (>= 1.10.4), GOfuncR (>= 1.1.2), grDevices, stats, graphics, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) NeedsCompilation: yes Title: Gene expression enrichment in human brain regions Description: The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function 'aba_enrich' integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project. 'aba_enrich' interfaces the ontology enrichment software FUNC to perform the statistical analyses. Additional functions provided in this package like 'get_expression' and 'plot_expression' facilitate exploring the expression data, and besides the standard candidate vs. background gene set enrichment, also three additional tests are implemented, e.g. for cases when genes are ranked instead of divided into candidate and background. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr PackageStatus: Deprecated Package: ProteomicsAnnotationHubData Version: 1.26.0 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocManager, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 Title: Transform public proteomics data resources into Bioconductor Data Structures Description: These recipes convert a variety and a growing number of public proteomics data sets into easily-used standard Bioconductor data structures. biocViews: DataImport, Proteomics Author: Gatto Laurent [aut, cre], Sonali Arora [aut] Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProteomicsAnnotationHubData VignetteBuilder: knitr BugReports: https://github.com/lgatto/ProteomicsAnnotationHubData/issues PackageStatus: Deprecated Package: tofsims Version: 1.24.0 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, alsace, signal, KernSmooth, graphics, grDevices, stats LinkingTo: Rcpp, RcppArmadillo Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData, BiocParallel, RColorBrewer Enhances: parallel License: GPL-3 NeedsCompilation: yes Title: Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data Description: This packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality. biocViews: ImmunoOncology, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber URL: https://github.com/lorenzgerber/tofsims VignetteBuilder: knitr BugReports: https://github.com/lorenzgerber/tofsims/issues PackageStatus: Deprecated Package: GenoGAM Version: 2.14.0 Depends: R (>= 3.5), SummarizedExperiment (>= 1.1.19), HDF5Array (>= 1.8.0), rhdf5 (>= 2.21.6), S4Vectors (>= 0.23.18), Matrix (>= 1.2-8), data.table (>= 1.9.4) Imports: Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools (>= 1.18.2), GenomicRanges (>= 1.23.16), BiocParallel (>= 1.5.17), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30), Biostrings (>= 2.39.14), DelayedArray (>= 0.3.19), methods, stats LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter (>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: A GAM based framework for analysis of ChIP-Seq data Description: This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals. biocViews: Regression, DifferentialPeakCalling, ChIPSeq, DifferentialExpression, Genetics, Epigenetics, WholeGenome, ChipOnChip, ImmunoOncology Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut] Maintainer: Georg Stricker URL: https://github.com/gstricker/GenoGAM VignetteBuilder: knitr BugReports: https://github.com/gstricker/GenoGAM/issues PackageStatus: Deprecated Package: TSRchitect Version: 1.22.0 Depends: R (>= 3.5) Imports: AnnotationHub, BiocGenerics, BiocParallel, dplyr, GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools, IRanges, methods, readxl, Rsamtools (>= 1.14.3), rtracklayer, S4Vectors, SummarizedExperiment, tools, utils Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown License: GPL-3 NeedsCompilation: no Title: Promoter identification from large-scale TSS profiling data Description: In recent years, large-scale transcriptional sequence data has yielded considerable insights into the nature of gene expression and regulation in eukaryotes. Techniques that identify the 5' end of mRNAs, most notably CAGE, have mapped the promoter landscape across a number of model organisms. Due to the variability of TSS distributions and the transcriptional noise present in datasets, precisely identifying the active promoter(s) for genes from these datasets is not straightforward. TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq). In addition, (new with version 1.3.0) TSRchitect provides the ability to import aligned EST and cDNA data. Along with the coordiantes of identified TSRs, TSRchitect also calculates the width, abundance and two forms of the Shape Index, and handles biological replicates for expression profiling. Finally, TSRchitect imports annotation files, allowing the user to associate identified promoters with genes and other genomic features. Three detailed examples of TSRchitect's utility are provided in the User's Guide, included with this package. biocViews: Clustering, FunctionalGenomics, GeneExpression, GeneRegulation, GenomeAnnotation, Sequencing, Transcription Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph] Krishnakumar Sridharan [ctb] Maintainer: R. Taylor Raborn URL: https://github.com/brendelgroup/tsrchitect VignetteBuilder: knitr BugReports: https://github.com/brendelgroup/tsrchitect/issues PackageStatus: Deprecated Package: coexnet Version: 1.18.0 Depends: R (>= 3.6) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr Package: Onassis Version: 1.18.0 Depends: R (>= 4.0), rJava, OnassisJavaLibs Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils, AnnotationDbi, RCurl, stats, DT, data.table, knitr, Rtsne, dendextend, clusteval, ggplot2, ggfortify Suggests: BiocStyle, rmarkdown, htmltools, org.Hs.eg.db, gplots, GenomicRanges, kableExtra License: GPL-2 Title: OnASSIs Ontology Annotation and Semantic SImilarity software Description: A package that allows the annotation of text with ontology terms (mainly from OBO ontologies) and the computation of semantic similarity measures based on the structure of the ontology between different annotated samples. biocViews: Annotation, DataImport, Clustering, Network, Software, GeneTarget Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr PackageStatus: Deprecated Package: perturbatr Version: 1.16.0 Depends: R (>= 3.5), methods, stats Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph, foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid, utils, graphics, scales, magrittr, formula.tools, rlang Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL-3 Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens Description: perturbatr does stage-wise analysis of large-scale genetic perturbation screens for integrated data sets consisting of multiple screens. For multiple integrated perturbation screens a hierarchical model that considers the variance between different biological conditions is fitted. The resulting list of gene effects is then further extended using a network propagation algorithm to correct for false negatives. biocViews: ImmunoOncology, Regression, CellBasedAssays, Network Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/cbg-ethz/perturbatr VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/perturbatr/issues PackageStatus: Deprecated Package: RNASeqR Version: 1.14.1 Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib, FactoMineR, factoextra, corrplot, PerformanceAnalytics, reshape2, DESeq2, systemPipeR, systemPipeRdata, clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr, pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings, parallel Suggests: knitr, rmarkdown, png, grid, RNASeqRData License: Artistic-2.0 NeedsCompilation: no Title: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Description: This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: "RNASeqRParam S4 Object Creation", "Environment Setup", "Quality Assessment", "Reads Alignment & Quantification", "Gene-level Differential Analyses" and "Functional Analyses". Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, GeneExpression, GeneSetEnrichment, Alignment, QualityControl, DifferentialExpression, FunctionalPrediction, ExperimentalDesign, GO, KEGG, Visualization, Normalization, Pathways, Clustering, ImmunoOncology Author: Kuan-Hao Chao Maintainer: Kuan-Hao Chao URL: https://github.com/HowardChao/RNASeqR SystemRequirements: RNASeqR only support Linux and macOS. Window is not supported. Python2 is highly recommended. If your machine is Python3, make sure '2to3' command is available. VignetteBuilder: knitr BugReports: https://github.com/HowardChao/RNASeqR/issues Package: XCIR Version: 1.10.0 Depends: methods Imports: stats, utils, tools, data.table, Biostrings, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 Title: XCI-inference Description: Models and tools for subject level analysis of X chromosome inactivation (XCI) and XCI-escape inference. biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage Author: Renan Sauteraud, Dajiang Liu Maintainer: Renan Sauteraud URL: https://github.com/SRenan/XCIR VignetteBuilder: knitr BugReports: https://github.com/SRenan/XCIR/issues Package: Autotuner Version: 1.10.0 Depends: R (>= 4.0.0), methods, Biobase, MSnbase (>= 2.14.2) Imports: RColorBrewer, mzR, assertthat, scales, entropy, cluster, grDevices, graphics, stats, utils Suggests: testthat (>= 2.1.0), covr, devtools, knitr, rmarkdown, mtbls2 License: MIT + file LICENSE Title: Automated parameter selection for untargeted metabolomics data processing Description: This package is designed to help faciliate data processing in untargeted metabolomics. To do this, the algorithm contained within the package performs statistical inference on raw data to come up with the best set of parameters to process the raw data. biocViews: MassSpectrometry, Metabolomics Author: Craig McLean Maintainer: Craig McLean URL: https://github.com/crmclean/Autotuner/ VignetteBuilder: knitr BugReports: https://github.com/crmclean/Autotuner/issues PackageStatus: Deprecated Package: MethCP Version: 1.10.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit, DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel Suggests: testthat, knitr, rmarkdown License: Artistic-2.0 Title: Differential methylation anlsysis for bisulfite sequencing data Description: MethCP is a differentially methylated region (DMR) detecting method for whole-genome bisulfite sequencing (WGBS) data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse Author: Boying Gong [aut, cre] Maintainer: Boying Gong VignetteBuilder: knitr BugReports: https://github.com/boyinggong/methcp/issues