Package: a4 Version: 1.40.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats License: GPL-3 MD5sum: e2ac149c6780136ece005ede120035c3 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_13 git_last_commit: f5e7837 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4_1.40.0.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: 81 Package: a4Base Version: 1.40.0 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: aa474675e93490a8847b794b6af33aa2 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_13 git_last_commit: b2cd105 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4Base_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Base_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Base_1.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 73 Package: a4Classif Version: 1.40.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 18794bef2876522232e1449fc9635c57 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_13 git_last_commit: 8fb3404 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4Classif_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Classif_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Classif_1.40.0.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: 30 Package: a4Core Version: 1.40.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 8a703f3d78ecee9d5499d917140ab7c4 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_13 git_last_commit: e1ca087 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4Core_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Core_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Core_1.40.0.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: 18 Package: a4Preproc Version: 1.40.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 4e2dbdacffcc1aac164978ff4eb0ff7c 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_13 git_last_commit: 0fa3d10 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4Preproc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Preproc_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Preproc_1.40.0.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: 7 Package: a4Reporting Version: 1.40.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: db9566798e7dca1875f68099ba7efba7 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_13 git_last_commit: 863239f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/a4Reporting_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Reporting_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Reporting_1.40.0.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: ABAEnrichment Version: 1.22.0 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) Archs: i386, x64 MD5sum: 820a5bd18687e34d4385994c59fd836e 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 git_url: https://git.bioconductor.org/packages/ABAEnrichment git_branch: RELEASE_3_13 git_last_commit: fcc29c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ABAEnrichment_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABAEnrichment_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABAEnrichment_1.22.0.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html vignetteTitles: Introduction to ABAEnrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R suggestsMe: ABAData dependencyCount: 60 Package: ABarray Version: 1.60.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: bda82504584e53f0700375e698a4cd85 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_13 git_last_commit: 7e6ed61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ABarray_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABarray_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABarray_1.60.0.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: 17 Package: abseqR Version: 1.10.0 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: c5b055b18ba9fb04532b7d5d19e5c91c 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_13 git_last_commit: 9ad3c88 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/abseqR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/abseqR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/abseqR_1.10.0.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: 109 Package: ABSSeq Version: 1.46.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: 0da0d9b8a382e84fa85baee07c00133a 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_13 git_last_commit: aae4a91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ABSSeq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABSSeq_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABSSeq_1.46.0.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: 1.22.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 862a91f5db1764982ffbe43c4bb44b42 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_13 git_last_commit: db084f5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/acde_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/acde_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/acde_1.22.0.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 importsMe: coexnet dependencyCount: 3 Package: ACE Version: 1.10.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 736112617e52d5bbee29a269e28f6bbc 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_13 git_last_commit: 791411a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ACE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ACE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ACE_1.10.0.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: 80 Package: aCGH Version: 1.70.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: a8d7188a71f549c0428d5887040dd595 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_13 git_last_commit: e412576 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/aCGH_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aCGH_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aCGH_1.70.0.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: 17 Package: ACME Version: 2.48.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 2c2356812f3def5c23ef7f11292859b0 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_13 git_last_commit: ae84fa6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ACME_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ACME_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ACME_2.48.0.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: 7 Package: ADaCGH2 Version: 2.32.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 52ed5d30351b9daf10e1dc49ba8be8be 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 . Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_13 git_last_commit: 43bdeb8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ADaCGH2_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADaCGH2_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADaCGH2_2.32.0.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: 101 Package: ADAM Version: 1.8.0 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 License: GPL (>= 2) Archs: i386, x64 MD5sum: 6f1e08b7280d169f21c8205fe127d0a5 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 Author: André Luiz Molan , Giordano Bruno Sanches Seco , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_13 git_last_commit: e062ab5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ADAM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADAM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADAM_1.8.0.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: 84 Package: ADAMgui Version: 1.8.0 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: BiocStyle License: GPL (>= 2) MD5sum: 9baf6690b94da51c92bbfad9edecdf18 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 , André Luiz Molan , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho 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_13 git_last_commit: 01ab40b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ADAMgui_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADAMgui_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADAMgui_1.8.0.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: 176 Package: adductomicsR Version: 1.8.0 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: dde23d69cca629c593a45812a68c646b 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_13 git_last_commit: 778448c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/adductomicsR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/adductomicsR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/adductomicsR_1.8.0.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: 1.2.0 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: 422a4052353fce8d6e2b2778314043e9 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_13 git_last_commit: 7c93ba7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ADImpute_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADImpute_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADImpute_1.2.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 52 Package: adSplit Version: 1.62.0 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: i386, x64 MD5sum: ed4946aa72b1d5387b8947b7a3e77e65 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_13 git_last_commit: 2181a05 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/adSplit_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/adSplit_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/adSplit_1.62.0.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: 55 Package: AffiXcan Version: 1.10.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 7b5434a4dad2cb8376ff074a9fd5607a 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_13 git_last_commit: 80665cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AffiXcan_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffiXcan_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffiXcan_1.10.0.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: 54 Package: affxparser Version: 1.64.1 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: f78d57c891d7ac4d50a0183d57bf3f32 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_13 git_last_commit: 7863cf7 git_last_commit_date: 2021-09-09 Date/Publication: 2021-09-12 source.ver: src/contrib/affxparser_1.64.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/affxparser_1.64.1.zip mac.binary.ver: bin/macosx/contrib/4.1/affxparser_1.64.1.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: 1.70.0 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: i386, x64 MD5sum: 82475b224f15947a29bb20f57dc754df 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_13 git_last_commit: 9c32d61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affy_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affy_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affy_1.70.0.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, affyPara, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, bgx, Cormotif, DrugVsDisease, dualKS, 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, MAQCsubsetAFX, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, coexnet, 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 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: 12 Package: affycomp Version: 1.68.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 9dfa85153c9f716d938d110bc297d2f2 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_13 git_last_commit: 8b02704 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affycomp_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affycomp_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affycomp_1.68.0.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: 7 Package: AffyCompatible Version: 1.52.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 56ca6779f8f37b6bafef77cac749b726 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_13 git_last_commit: 7dc4c06 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AffyCompatible_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffyCompatible_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffyCompatible_1.52.0.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: 20 Package: affyContam Version: 1.50.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 305d215ef67ef4b77cc061dbd81d3a8b 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_13 git_last_commit: bb3b0e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affyContam_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyContam_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyContam_1.50.0.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: 15 Package: affycoretools Version: 1.64.0 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: 158cfe006d0bd8f924a0d2dc7937699d 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_13 git_last_commit: ed4f10c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affycoretools_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affycoretools_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affycoretools_1.64.0.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: 188 Package: affyILM Version: 1.44.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: d3970dab4f20df85e8d724403d722f71 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_13 git_last_commit: 5d0305d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affyILM_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyILM_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyILM_1.44.0.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: 27 Package: affyio Version: 1.62.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 4bfadfb205c3b1473de05353aaffb5aa 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_13 git_last_commit: caa75be git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affyio_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyio_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyio_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.66.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: 40bafdc8dc4fe5b97d1d02c8e0bba2e8 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_13 git_last_commit: ff6d6f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affylmGUI_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affylmGUI_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affylmGUI_1.66.0.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: 58 Package: affyPara Version: 1.51.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: b93cb65ed755bfad43699ee7c6080e7d NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de git_url: https://git.bioconductor.org/packages/affyPara git_branch: master git_last_commit: a919225 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/affyPara_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyPara_1.51.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyPara_1.51.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R dependencyCount: 50 Package: affyPLM Version: 1.68.0 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: i386, x64 MD5sum: 8eea702e7fd9c62efe5c0c08017e5b15 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_13 git_last_commit: 492a50a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/affyPLM_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyPLM_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyPLM_1.68.0.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: 26 Package: AffyRNADegradation Version: 1.38.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: bfab6a1ede8973a84e8eef7c9d3ed57c 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_13 git_last_commit: fee09ea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AffyRNADegradation_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffyRNADegradation_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffyRNADegradation_1.38.0.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: 13 Package: AGDEX Version: 1.40.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 85f854216b2860d1f2e4e604846746f8 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_13 git_last_commit: a2c2223 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AGDEX_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AGDEX_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AGDEX_1.40.0.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: 51 Package: aggregateBioVar Version: 1.2.0 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: d06c41b1c5251f6e548a17e4cac5e127 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_13 git_last_commit: 70ba9dd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/aggregateBioVar_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aggregateBioVar_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aggregateBioVar_1.2.0.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: 40 Package: agilp Version: 3.24.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 59c0289df832bba9bee4264195f54f28 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_13 git_last_commit: 3346a25 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/agilp_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/agilp_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/agilp_3.24.0.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: 2.42.0 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: 57df1b9fccb0120161ddd90533e75a4b 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_13 git_last_commit: 5f453f7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AgiMicroRna_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AgiMicroRna_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AgiMicroRna_2.42.0.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: 189 Package: AIMS Version: 1.24.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: dca02f8473ed734a4913b69646b96698 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_13 git_last_commit: 78a7be5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AIMS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AIMS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AIMS_1.24.0.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: 12 Package: airpart Version: 1.0.1 Depends: R (>= 4.0) 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 License: GPL-2 MD5sum: ecb0ae32c2e2f9f9bed84ff1a6c42c66 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/airpart git_branch: RELEASE_3_13 git_last_commit: 39ff0e5 git_last_commit_date: 2021-08-23 Date/Publication: 2021-08-24 source.ver: src/contrib/airpart_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/airpart_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/airpart_1.0.1.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: 1.24.0 Depends: methods, stats, zCompositions, Rfast Imports: BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: file LICENSE MD5sum: 23f3608fbd47f707dbc4c13cbb1a5a06 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. 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_13 git_last_commit: a41b778 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ALDEx2_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ALDEx2_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ALDEx2_1.24.0.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 suggestsMe: propr dependencyCount: 45 Package: alevinQC Version: 1.8.0 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 Suggests: knitr, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 3bad4dc843ed3b31c0e6bb43bd1a6700 NeedsCompilation: no 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] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_13 git_last_commit: b6880ce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/alevinQC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alevinQC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alevinQC_1.8.0.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: 89 Package: AllelicImbalance Version: 1.30.0 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: ddd690215d718ecdc98117d4aba6f0d0 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_13 git_last_commit: 5e00a1f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AllelicImbalance_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AllelicImbalance_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AllelicImbalance_1.30.0.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: 147 Package: AlphaBeta Version: 1.6.1 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: a3d911a6feb7e12eb7f90193d1752ccb 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_13 git_last_commit: cdfa5f6 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/AlphaBeta_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/AlphaBeta_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/AlphaBeta_1.6.1.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: 1.18.0 Depends: R (>= 3.3) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 2929e9cb8297f544b46d60d323a49089 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_13 git_last_commit: 06257d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/alpine_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alpine_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alpine_1.18.0.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: 101 Package: ALPS Version: 1.5.0 Depends: R (>= 3.6) Imports: assertthat, BiocParallel, ChIPseeker, corrplot, data.table, dplyr, GenomicRanges, GGally, genefilter, gghalves, ggplot2, ggseqlogo, Gviz, magrittr, org.Hs.eg.db, plyr, reshape2, rtracklayer, stats, stringr, tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, utils Suggests: knitr, rmarkdown, ComplexHeatmap, circlize, testthat License: MIT + file LICENSE MD5sum: fb71399778ec443bed72deaf8a54b4b1 NeedsCompilation: no Title: AnaLysis routines for ePigenomicS data Description: The package provides analysis and publication quality visualization routines for genome-wide epigenomics data such as histone modification or transcription factor ChIP-seq, ATAC-seq, DNase-seq etc. The functions in the package can be used with any type of data that can be represented with bigwig files at any resolution. The goal of the ALPS is to provide analysis tools for most downstream analysis without leaving the R environment and most tools in the package require a minimal input that can be prepared with basic R, unix or excel skills. biocViews: Epigenetics, Sequencing, ChIPSeq, ATACSeq, Visualization, Transcription, HistoneModification Author: Venu Thatikonda, Natalie Jäger Maintainer: Venu Thatikonda URL: https://github.com/itsvenu/ALPS VignetteBuilder: knitr BugReports: https://github.com/itsvenu/ALPS/issues git_url: https://git.bioconductor.org/packages/ALPS git_branch: master git_last_commit: 6ea885b git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/ALPS_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ALPS_1.5.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ALPS_1.5.0.tgz vignettes: vignettes/ALPS/inst/doc/ALPS-vignette.html vignetteTitles: ALPS-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALPS/inst/doc/ALPS-vignette.R dependencyCount: 196 Package: AlpsNMR Version: 3.2.3 Depends: R (>= 4.0), dplyr (>= 0.7.5), future (>= 1.10.0), magrittr (>= 1.5) Imports: utils, graphics, stats, grDevices, signal (>= 0.7-6), assertthat (>= 0.2.0), rlang (>= 0.3.0.1), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), readxl (>= 1.1.0), plyr (>= 1.8.4), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), GGally (>= 1.4.0), mixOmics (>= 6.3.2), matrixStats (>= 0.54.0), writexl (>= 1.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), ggrepel (>= 0.8.0), pcaPP (>= 1.9-73), furrr (>= 0.1.0), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), zip (>= 2.0.4), tidyselect (>= 0.2.5), vctrs (>= 0.3.0), BiocParallel, SummarizedExperiment, S4Vectors Suggests: DT (>= 0.5), testthat (>= 2.0.0), plotly (>= 4.7.1), ChemoSpec, knitr License: MIT + file LICENSE MD5sum: 0cda75eee828f0e1c09342ff4552c349 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] (), Francisco Madrid Gambin [aut] (), Luis Fernandez [aut, cre] (), Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Luis Fernandez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_13 git_last_commit: 92db448 git_last_commit_date: 2021-09-16 Date/Publication: 2021-09-19 source.ver: src/contrib/AlpsNMR_3.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/AlpsNMR_3.2.3.zip mac.binary.ver: bin/macosx/contrib/4.1/AlpsNMR_3.2.3.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: 150 Package: alsace Version: 1.28.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice, knitr License: GPL (>= 2) MD5sum: 8b86ccfc02ccd7edf39b355d4811e9a0 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens URL: https://github.com/rwehrens/alsace VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alsace git_branch: RELEASE_3_13 git_last_commit: 03344a1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/alsace_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alsace_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alsace_1.28.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: tofsims dependencyCount: 8 Package: altcdfenvs Version: 2.54.0 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: 5c5b04cff87de11467bc04488b36ae2a 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_13 git_last_commit: fbe5728 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/altcdfenvs_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/altcdfenvs_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/altcdfenvs_2.54.0.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: 27 Package: AMARETTO Version: 1.8.0 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: 508c18b6d4b6cfe066c8577d345484fd 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_13 git_last_commit: 3a94300 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AMARETTO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AMARETTO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AMARETTO_1.8.0.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: 1.18.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 80fcf1be99e98f0142e53b7b0948474a 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_13 git_last_commit: b3cec6e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AMOUNTAIN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AMOUNTAIN_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AMOUNTAIN_1.18.0.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: 1.14.0 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: i386, x64 MD5sum: e870d031eee9696c3b1fa9e81700d09c 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_13 git_last_commit: e994104 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/amplican_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/amplican_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/amplican_1.14.0.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: 99 Package: Anaquin Version: 2.16.0 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: 1f29ca74d362bf16c74b231efb2666af 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_13 git_last_commit: a2406fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Anaquin_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Anaquin_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Anaquin_2.16.0.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: 1.2.2 Imports: stats, MASS, nloptr, Rdpack, phyloseq, microbiome Suggests: knitr, tidyverse, testthat, DT, magrittr, qwraps2 (>= 0.5.0), rmarkdown License: Artistic-2.0 MD5sum: 52be737254459224c2d72509f1fce9d4 NeedsCompilation: no Title: Analysis of compositions of microbiomes with bias correction Description: ANCOMBC is a package for normalizing the microbial observed abundance data due to unequal sampling fractions across samples, and identifying taxa (e.g. phyla, families, genera, species, etc.) that are differentially abundant with respect to the covariate of interest (e.g. study groups) between two or more groups of multiple samples. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (), Shyamal Das Peddada [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_13 git_last_commit: fa2dd53 git_last_commit_date: 2021-08-13 Date/Publication: 2021-08-15 source.ver: src/contrib/ANCOMBC_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/ANCOMBC_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/ANCOMBC_1.2.2.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOMBC.html vignetteTitles: ANCOMBC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOMBC.R dependencyCount: 88 Package: AneuFinder Version: 1.20.0 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: i386, x64 MD5sum: 53ca2dbbb874c91f4a140c8061405c25 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_13 git_last_commit: 1c7aed6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AneuFinder_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AneuFinder_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AneuFinder_1.20.0.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: 89 Package: ANF Version: 1.14.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 82aa86db8efa639ca7b01fc3a4aed243 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_13 git_last_commit: cec6e3e git_last_commit_date: 2021-05-19 Date/Publication: 2021-06-13 source.ver: src/contrib/ANF_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ANF_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ANF_1.14.0.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: 1.8.1 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, reactable, utils, limma, methods, stats, tibble, biomformat, umap, Matrix, GUniFrac Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: cb593b25ad849989fadd66b0e01e8738 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_13 git_last_commit: 5a3dc66 git_last_commit_date: 2021-06-02 Date/Publication: 2021-06-03 source.ver: src/contrib/animalcules_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/animalcules_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/animalcules_1.8.1.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: 184 Package: annaffy Version: 1.64.2 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 89b99be5e6bb3f1bc439a541285727df 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_13 git_last_commit: e4b2445 git_last_commit_date: 2021-06-19 Date/Publication: 2021-06-20 source.ver: src/contrib/annaffy_1.64.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/annaffy_1.64.2.zip mac.binary.ver: bin/macosx/contrib/4.1/annaffy_1.64.2.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: 48 Package: annmap Version: 1.34.0 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: b6cafcdb5d1153daecdc95030f9398c4 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. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_13 git_last_commit: c79bf2e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/annmap_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/annmap_1.34.0.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: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 67 Package: annotate Version: 1.70.0 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: 1e911d1dce3a13cc46f89b020be4bedf 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_13 git_last_commit: 9c8cb9d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/annotate_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotate_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annotate_1.70.0.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, ScISI, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, MGFR, phenoTest, qpgraph, RpsiXML, ScISI, systemPipeR, tigre, UMI4Cats, geneExpressionFromGEO, GOxploreR suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pageRank, pcxn, PhosR, RnBeads, siggenes, SummarizedExperiment, 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.Pf.plasmo.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, maGUI, MOSS, optCluster dependencyCount: 48 Package: AnnotationDbi Version: 1.54.1 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: d4dbc895bd2de2dca145ad86d96a28c9 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_13 git_last_commit: 0040118 git_last_commit_date: 2021-06-07 Date/Publication: 2021-06-08 source.ver: src/contrib/AnnotationDbi_1.54.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationDbi_1.54.1.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationDbi_1.54.1.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, 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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.Pf.plasmo.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, compEpiTools, conclus, consensusDE, cosmosR, crisprseekplus, CrispRVariants, crossmeta, debrowser, derfinder, DominoEffect, DOSE, EDASeq, eegc, EnrichmentBrowser, ensembldb, erma, esATAC, FRASER, GA4GHshiny, gage, GAPGOM, genefilter, geneplotter, GeneTonic, geneXtendeR, GenVisR, ggbio, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gpart, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, InPAS, interactiveDisplay, IRISFGM, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methyAnalysis, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, MLP, MSEADbi, MSnID, multiGSEA, multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, NetSAM, 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, ScISI, scPipe, scruff, scTensor, SGSeq, signatureSearch, simplifyEnrichment, singleCellTK, SLGI, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, tigre, trackViewer, trena, tricycle, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovine.db0, bovinecdf, bsubtiliscdf, canine.db, canine.db0, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chicken.db0, chickencdf, chimp.db0, 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, ecoliK12.db0, ecoliSakai.db0, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, fly.db0, GenomicState, GGHumanMethCancerPanelv1.db, GO.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, human.db0, 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, malaria.db0, 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, mouse.db0, 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, 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.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, 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, rat.db0, 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, rhesus.db0, 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.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.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.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, worm.db0, xenopus.db0, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast.db0, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafish.db0, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, ppiData, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, DIscBIO, jetset, MetaIntegrator, netgsa, pathfindR, prioGene, pulseTD, RobLoxBioC, WGCNA suggestsMe: APAlyzer, autonomics, bambu, BiocGenerics, BiocOncoTK, CellTrails, cicero, cola, csaw, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, GA4GHclient, gCrisprTools, GeneRegionScan, GenomicRanges, iSEEu, limma, MutationalPatterns, oligo, OUTRIDER, piano, Pigengene, pRoloc, quantiseqr, R3CPET, recount, RGalaxy, sigPathway, SummarizedExperiment, TFutils, tidybulk, topconfects, weitrix, wiggleplotr, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, CALANGO, conos, cRegulome, DGCA, easylabel, pagoda2, rliger dependencyCount: 45 Package: AnnotationFilter Version: 1.16.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db License: Artistic-2.0 MD5sum: 8fa71403018268d536fcbc0c52e54a03 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_13 git_last_commit: e4e4425 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AnnotationFilter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationFilter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationFilter_1.16.0.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, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, drugTargetInteractions, ggbio, QFeatures, TVTB, GenomicDistributionsData, utr.annotation suggestsMe: GenomicDistributions, TFutils, wiggleplotr dependencyCount: 18 Package: AnnotationForge Version: 1.34.1 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: f32a32e3bb7e148639efe09a8f7d6ff9 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_13 git_last_commit: a2fccf4 git_last_commit_date: 2021-10-11 Date/Publication: 2021-10-12 source.ver: src/contrib/AnnotationForge_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationForge_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationForge_1.34.1.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: 47 Package: AnnotationHub Version: 3.0.2 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 Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 1723427f6cad829e707fd3fb1f4a437a 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_13 git_last_commit: cdca7b7 git_last_commit_date: 2021-10-13 Date/Publication: 2021-10-14 source.ver: src/contrib/AnnotationHub_3.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationHub_3.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHub_3.0.2.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/CreateAHubPackage.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Creating A Hub Package: ExperimentHub or AnnotationHub, 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/CreateAHubPackage.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, LRcell, ProteomicsAnnotationHubData, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, MetaGxBreast, MetaGxOvarian, NestLink, sesameData, tartare, annotation, sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows importsMe: annotatr, circRNAprofiler, customCMPdb, dmrseq, EWCE, GenomicScores, GSEABenchmarkeR, gwascat, MACSr, MSnID, psichomics, pwOmics, regutools, REMP, restfulSE, scmeth, scTensor, TSRchitect, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, grasp2db, metaboliteIDmapping, adductData, alpineData, biscuiteerData, celldex, chipseqDBData, curatedMetagenomicData, curatedTCGAData, depmap, DropletTestFiles, FieldEffectCrc, GenomicDistributionsData, HCAData, HMP16SData, HMP2Data, mcsurvdata, MetaGxPancreas, scpdata, scRNAseq, SingleCellMultiModal, spatialLIBD, TENxBrainData, TENxBUSData, TENxPBMCData, TCGAWorkflow, utr.annotation suggestsMe: BgeeCall, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, DNAshapeR, dupRadar, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, GenomicRanges, GOSemSim, maser, MIRA, MSnbase, multicrispr, OrganismDbi, recountmethylation, satuRn, VariantAnnotation, AHEnsDbs, ENCODExplorerData, gwascatData, HarmonizedTCGAData, SingleRBook dependencyCount: 86 Package: AnnotationHubData Version: 1.22.0 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 License: Artistic-2.0 MD5sum: 645494a68c78ef6557d677cd7c16a701 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_13 git_last_commit: c2a76fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AnnotationHubData_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationHubData_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHubData_1.22.0.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: 133 Package: annotationTools Version: 1.66.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: 3495f307472834d2c1ff60b277ec7a15 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_13 git_last_commit: 34aad15 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/annotationTools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotationTools_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annotationTools_1.66.0.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: 7 Package: annotatr Version: 1.18.1 Depends: R (>= 3.4.0) 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: 1e64c5197247dc95d69669dbab02bccb 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_13 git_last_commit: 0066d80 git_last_commit_date: 2021-07-12 Date/Publication: 2021-07-13 source.ver: src/contrib/annotatr_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotatr_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/annotatr_1.18.1.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth suggestsMe: ramr dependencyCount: 141 Package: anota Version: 1.40.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 0eea6a1fc6403ae1c87f69e917243cb8 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_13 git_last_commit: ef88b0a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/anota_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/anota_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/anota_1.40.0.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: 51 Package: anota2seq Version: 1.14.0 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: 6c045e2428867c986bc6005ab1d15cbc 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_13 git_last_commit: d5aad21 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/anota2seq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/anota2seq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/anota2seq_1.14.0.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: 1.32.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: f2ea29f1a395958e04d62e14057ee127 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_13 git_last_commit: 9e83e6d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/antiProfiles_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/antiProfiles_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/antiProfiles_1.32.0.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: 1.4.1 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), tibble, tidyselect, tidyr, rlang, BiocManager Suggests: knitr, rmarkdown, testthat, withr, readr License: Artistic-2.0 MD5sum: b6703f54d31d850f2dd7045a48af2ff9 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_13 git_last_commit: d4bcc97 git_last_commit_date: 2021-06-21 Date/Publication: 2021-06-22 source.ver: src/contrib/AnVIL_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVIL_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVIL_1.4.1.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R dependsOnMe: cBioPortalData importsMe: AnVILPublish dependencyCount: 39 Package: AnVILBilling Version: 1.2.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle License: Artistic-2.0 MD5sum: 127e9f4bbb5291f41756c02e84acfb59 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_13 git_last_commit: c339a21 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AnVILBilling_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVILBilling_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVILBilling_1.2.0.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: 90 Package: AnVILPublish Version: 1.2.0 Imports: AnVIL, httr, jsonlite, rmarkdown, whisker, tools, utils, stats, Suggests: knitr, BiocStyle, BiocManager License: Artistic-2.0 MD5sum: 3e5a75448fb176065d6c3ab0cba833ff 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_13 git_last_commit: ca7c985 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AnVILPublish_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVILPublish_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVILPublish_1.2.0.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: 54 Package: APAlyzer Version: 1.6.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, ensembldb, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 MD5sum: 10a8883cf6002eae64909ac9224fab04 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], Chuwei Zhong [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_13 git_last_commit: e2978dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/APAlyzer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/APAlyzer_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/APAlyzer_1.6.0.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: 135 Package: apComplex Version: 2.58.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: bf99032cb2c13c8724fd7ad7641cbca1 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_13 git_last_commit: 9d396a7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/apComplex_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/apComplex_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/apComplex_2.58.0.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 dependsOnMe: ScISI dependencyCount: 52 Package: apeglm Version: 1.14.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 28d703ecf247b6ba06aee3d35a0b9ced 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_13 git_last_commit: c64f333 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/apeglm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/apeglm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/apeglm_1.14.0.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 suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq dependencyCount: 37 Package: appreci8R Version: 1.10.0 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: 2779c288e20348b97814ddec06371764 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_13 git_last_commit: d1399fc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/appreci8R_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/appreci8R_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/appreci8R_1.10.0.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: 159 Package: aroma.light Version: 3.22.0 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: 5ca5d02161119049876fefbba0f113ff 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_13 git_last_commit: e4a668e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/aroma.light_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aroma.light_3.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aroma.light_3.22.0.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: 1.52.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 6d6edd2c2c83c54aaa60274e4c829174 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_13 git_last_commit: bac4837 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ArrayExpress_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ArrayExpress_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpress_1.52.0.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 suggestsMe: Hiiragi2013, bapred dependencyCount: 56 Package: ArrayExpressHTS Version: 1.42.0 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: 67a8ed79c0aa34f6e115f9c81aa6bc1b 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_13 git_last_commit: 9a41140 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ArrayExpressHTS_1.42.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpressHTS_1.42.0.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: 139 Package: arrayMvout Version: 1.50.0 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: dfbae927c8d3594a1d74675ac0eb0a97 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_13 git_last_commit: c59319e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/arrayMvout_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayMvout_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayMvout_1.50.0.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: 165 Package: arrayQuality Version: 1.70.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 3d2cdf4eeafe03513a2d48607fc0662e 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_13 git_last_commit: f319a3a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/arrayQuality_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayQuality_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayQuality_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.48.0 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: 2c393461d95c61c64657afdb57b7ae5f 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_13 git_last_commit: 61bf05b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/arrayQualityMetrics_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayQualityMetrics_3.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayQualityMetrics_3.48.0.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: 124 Package: ARRmNormalization Version: 1.32.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: d13e5a6ecaeecccade2b1459d4fd24fe 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_13 git_last_commit: 242d488 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ARRmNormalization_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ARRmNormalization_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ARRmNormalization_1.32.0.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: 1.10.2 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: c69430077516d9d29b17445151f8ac6e 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_13 git_last_commit: 9f507a7 git_last_commit_date: 2021-07-14 Date/Publication: 2021-07-15 source.ver: src/contrib/artMS_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/artMS_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.1/artMS_1.10.2.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: 134 Package: ASAFE Version: 1.18.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: b203c6d3f42c5dbcd91bfc45ab7a62ba 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_13 git_last_commit: d7d0980 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASAFE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASAFE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASAFE_1.18.0.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: 1.36.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 42ccf68cd0c8075438aef5b5be423803 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_13 git_last_commit: e3fce2b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASEB_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASEB_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASEB_1.36.0.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: 1.26.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 48b644aa64ab40de612ecbdec148caae 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_13 git_last_commit: 9ace820 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASGSCA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASGSCA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASGSCA_1.26.0.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 suggestsMe: matrixpls dependencyCount: 9 Package: ASICS Version: 2.8.0 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: 90695fcac0424e80257ae3a91d9957e0 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_13 git_last_commit: 3169aa8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASICS_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASICS_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASICS_2.8.0.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: 87 Package: ASpediaFI Version: 1.6.0 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: ef09992826cc139f4fe9a979c11db3da 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_13 git_last_commit: a36105f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASpediaFI_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASpediaFI_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASpediaFI_1.6.0.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: 173 Package: ASpli Version: 2.2.0 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: d32084a06718cd58cb5223e5bb64b478 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_13 git_last_commit: 6dddfb0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ASpli_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASpli_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASpli_2.2.0.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: 161 Package: AssessORF Version: 1.10.0 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: e106ea9b614da3fee3f8604fc1c75170 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_13 git_last_commit: c43e459 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AssessORF_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AssessORF_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AssessORF_1.10.0.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: 2.10.1 Depends: stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 33258b973157ac5b7211c20f40126061 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], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_13 git_last_commit: 7bdf763 git_last_commit_date: 2021-09-10 Date/Publication: 2021-09-12 source.ver: src/contrib/ASSET_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASSET_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ASSET_2.10.1.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: 1.28.1 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: c1be993cd611969d94236bad376265a6 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_13 git_last_commit: 9e787ec git_last_commit_date: 2021-06-13 Date/Publication: 2021-06-15 source.ver: src/contrib/ASSIGN_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASSIGN_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ASSIGN_1.28.1.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: ATACseqQC Version: 1.16.0 Depends: R (>= 3.4), 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: 51b7515f1843b9e16810c3aacd04bc10 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_13 git_last_commit: e345154 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ATACseqQC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ATACseqQC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ATACseqQC_1.16.0.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: 160 Package: atSNP Version: 1.8.0 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: i386, x64 MD5sum: bb510740c2ed2e685ccb75f399c886a0 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_13 git_last_commit: 813c1c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/atSNP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/atSNP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/atSNP_1.8.0.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: 122 Package: attract Version: 1.44.0 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: b72c05eb2a84e52125d421c9d08e4994 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_13 git_last_commit: 6497b3f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/attract_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/attract_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/attract_1.44.0.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: 68 Package: AUCell Version: 1.14.0 Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, S4Vectors, 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: 4ddecb61938427c577caeb9491a0bab2 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_13 git_last_commit: 8849265 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AUCell_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AUCell_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AUCell_1.14.0.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 dependencyCount: 87 Package: autonomics Version: 1.0.1 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: d9721fb4f49ac21990b5f047e4e4c166 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_13 git_last_commit: 5df16f7 git_last_commit_date: 2021-05-25 Date/Publication: 2021-06-06 source.ver: src/contrib/autonomics_1.0.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/autonomics_1.0.1.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: Autotuner Version: 1.6.0 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 MD5sum: c4b42588f6b6fbf1b36ef4a2987528d3 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/Autotuner git_branch: RELEASE_3_13 git_last_commit: f373bec git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Autotuner_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Autotuner_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Autotuner_1.6.0.tgz vignettes: vignettes/Autotuner/inst/doc/Autotuner.html vignetteTitles: Autotuner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Autotuner/inst/doc/Autotuner.R dependencyCount: 80 Package: AWFisher Version: 1.6.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: i386, x64 MD5sum: 8d51cec9bd87fa11941da4fe8e040ddd 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_13 git_last_commit: d14025a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/AWFisher_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AWFisher_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AWFisher_1.6.0.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: 1.0.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 2dfc0d465d119ccb32d3ad3d0d635755 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_13 git_last_commit: e922ee7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/awst_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/awst_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/awst_1.0.0.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: 26 Package: BaalChIP Version: 1.18.0 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: 504fdc38a0fab2c9fd35f40b90001207 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_13 git_last_commit: 49e2ae7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BaalChIP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BaalChIP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BaalChIP_1.18.0.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: 101 Package: BAC Version: 1.52.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: cb59af22c729863afaa510d573221733 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_13 git_last_commit: 25895e4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BAC_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BAC_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BAC_1.52.0.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: 1.20.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 3b2c50d55c4e3603c4f65113cac8d120 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_13 git_last_commit: 629e321 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bacon_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bacon_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bacon_1.20.0.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: 47 Package: BADER Version: 1.30.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: 3b921cfd8dce79a30371fe396c628015 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_13 git_last_commit: 461f332 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BADER_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BADER_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BADER_1.30.0.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: 1.20.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 03aae3346e05539ca0378bf2c65123c3 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_13 git_last_commit: 833821c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BadRegionFinder_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BadRegionFinder_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BadRegionFinder_1.20.0.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: 98 Package: BAGS Version: 2.32.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 4fb4a137eef4f0fb3a4bbfcd297acf45 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_13 git_last_commit: d94e763 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BAGS_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BAGS_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BAGS_2.32.0.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: 8 Package: ballgown Version: 2.24.0 Depends: R (>= 3.1.1), 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 License: Artistic-2.0 MD5sum: eb30649e2da81f727f17a48064b4b686 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_13 git_last_commit: 34829e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ballgown_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ballgown_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ballgown_2.24.0.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 importsMe: RNASeqR suggestsMe: polyester, variancePartition dependencyCount: 82 Package: bambu Version: 1.2.1 Depends: R(>= 4.0.0), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, glmnet, Rsamtools, methods, Rcpp LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, rmarkdown, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, BSgenome, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 4784cb94161b01bc2d8ab4fd6816c6f4 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], 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_13 git_last_commit: 1c71eae git_last_commit_date: 2021-08-30 Date/Publication: 2021-08-31 source.ver: src/contrib/bambu_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/bambu_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/bambu_1.2.1.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 suggestsMe: NanoporeRNASeq dependencyCount: 105 Package: bamsignals Version: 1.24.0 Depends: R (>= 3.2.0) 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: i386, x64 MD5sum: 585a87fba672a129b71ccc44c1f6e103 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_13 git_last_commit: 91d1e61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bamsignals_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bamsignals_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bamsignals_1.24.0.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, hoardeR dependencyCount: 19 Package: BANDITS Version: 1.8.0 Depends: R (>= 3.6.0) 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: i386, x64 MD5sum: fac7fb95c2970c9c6ebec78d5197b072 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], Mark D. Robinson [aut]. 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_13 git_last_commit: a4ebc87 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BANDITS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BANDITS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BANDITS_1.8.0.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 dependencyCount: 78 Package: banocc Version: 1.16.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat License: MIT + file LICENSE MD5sum: 7cb4a2fbfd202424c88e31495bf44829 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_13 git_last_commit: cf8d0fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/banocc_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/banocc_1.16.0.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: 67 Package: barcodetrackR Version: 1.0.0 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: 16fd12da80a1ddc78692f9a0483b0120 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_13 git_last_commit: 875e093 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/barcodetrackR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/barcodetrackR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/barcodetrackR_1.0.0.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: 1.16.0 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: 70d4f390f493d450d459abfc1c9c485a 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_13 git_last_commit: ac2c7cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/basecallQC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basecallQC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basecallQC_1.16.0.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: 105 Package: BaseSpaceR Version: 1.36.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: fa3262b1c81eec6e851a403989a78f6f 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_13 git_last_commit: a99d258 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BaseSpaceR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BaseSpaceR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BaseSpaceR_1.36.0.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: 1.28.0 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: fec643a17dc72d231f2a8699889be61b 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_13 git_last_commit: 0ad4961 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Basic4Cseq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Basic4Cseq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Basic4Cseq_1.28.0.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: 48 Package: BASiCS Version: 2.4.0 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: i386, x64 MD5sum: 7257aa6221d6be253522ab6d14801722 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_13 git_last_commit: 385fa3d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BASiCS_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BASiCS_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BASiCS_2.4.0.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: 122 Package: BasicSTARRseq Version: 1.20.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 04d50f405452d882e4bde2c4b8cf0c24 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_13 git_last_commit: 6542fef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BasicSTARRseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BasicSTARRseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BasicSTARRseq_1.20.0.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: 38 Package: basilisk Version: 1.4.0 Imports: utils, methods, parallel, reticulate, dir.expiry, basilisk.utils Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: df6104e0eb1b6411b21bf6b5461c4a14 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_13 git_last_commit: 11d7d30 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/basilisk_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basilisk_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basilisk_1.4.0.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, dasper, densvis, MACSr, MOFA2, Rcwl, snifter, velociraptor, zellkonverter dependencyCount: 21 Package: basilisk.utils Version: 1.4.0 Imports: utils, methods, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: e729e3bd23a8048403fd768797b16876 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_13 git_last_commit: e74f4df git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/basilisk.utils_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basilisk.utils_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basilisk.utils_1.4.0.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: 1.8.1 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: i386, x64 MD5sum: 1a186bb9481808c27b90bf728742b345 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_13 git_last_commit: ac1b37a git_last_commit_date: 2021-08-11 Date/Publication: 2021-08-12 source.ver: src/contrib/batchelor_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/batchelor_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/batchelor_1.8.1.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: 49 Package: BatchQC Version: 1.20.0 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: 02238f995d0bdb6343ac96371ae74e5c 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_13 git_last_commit: 696d55c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BatchQC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BatchQC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BatchQC_1.20.0.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: 161 Package: BayesKnockdown Version: 1.18.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: f6df8a5ef81ba98d8a0413f93f0beff3 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_13 git_last_commit: d299049 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BayesKnockdown_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BayesKnockdown_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BayesKnockdown_1.18.0.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: 7 Package: BayesSpace Version: 1.2.1 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: i386, x64 MD5sum: f24d780ff4487a78484ae9a6e874dde4 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_13 git_last_commit: d1996f9 git_last_commit_date: 2021-09-17 Date/Publication: 2021-09-19 source.ver: src/contrib/BayesSpace_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BayesSpace_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BayesSpace_1.2.1.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: 134 Package: bayNorm Version: 1.10.0 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: i386, x64 MD5sum: 7f78214dd4af47da56d10dc570d93b6c 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_13 git_last_commit: cbf997b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bayNorm_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bayNorm_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bayNorm_1.10.0.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: 2.26.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: f292776d63393b6a0b68da118dbd36fd 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_13 git_last_commit: 44ebe60 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/baySeq_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/baySeq_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/baySeq_2.26.0.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: 1.22.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: eec4a3b5c875c8f6f66f3e5d65163864 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_13 git_last_commit: ca0ad63 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BBCAnalyzer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BBCAnalyzer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BBCAnalyzer_1.22.0.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: 98 Package: BCRANK Version: 1.54.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: a0c5619c81e19878844ab9678ecc1069 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_13 git_last_commit: 827b00a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BCRANK_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BCRANK_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BCRANK_1.54.0.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: 19 Package: bcSeq Version: 1.14.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: f9d67c85a00a77957153cf4d9fcfea27 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_13 git_last_commit: d9092ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bcSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bcSeq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bcSeq_1.14.0.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: 23 Package: BDMMAcorrect Version: 1.10.0 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: i386, x64 MD5sum: c50a8a732446be34363512d701905ff9 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_13 git_last_commit: c22bbaf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BDMMAcorrect_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BDMMAcorrect_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BDMMAcorrect_1.10.0.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: 64 Package: beachmat Version: 2.8.1 Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array License: GPL-3 Archs: i386, x64 MD5sum: ca6ae80e2258e0a8fec6451703703f95 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_13 git_last_commit: 5c9ef4d git_last_commit_date: 2021-08-10 Date/Publication: 2021-08-12 source.ver: src/contrib/beachmat_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/beachmat_2.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/beachmat_2.8.1.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: 17 Package: beadarray Version: 2.42.0 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: i386, x64 MD5sum: 67f115ec50e5340bed1d9d3887576702 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_13 git_last_commit: 028000c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/beadarray_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/beadarray_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/beadarray_2.42.0.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, beadarrayFilter importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI dependencyCount: 82 Package: beadarraySNP Version: 1.58.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 5f20bbe735373465db0e09ca9c690c1f 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_13 git_last_commit: 6da1ae1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/beadarraySNP_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/beadarraySNP_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/beadarraySNP_1.58.0.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: 19 Package: BeadDataPackR Version: 1.44.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: d8b269dbe2b6277f3c754d78e9457bae 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_13 git_last_commit: ca45790 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BeadDataPackR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BeadDataPackR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BeadDataPackR_1.44.0.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: 1.12.0 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: b61c054462d3181ccf8de5a8b313e41a 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_13 git_last_commit: 79b6626 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BEARscc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEARscc_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEARscc_1.12.0.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: 59 Package: BEAT Version: 1.30.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 49fc8a1fd2ac1a9403a1106051f9fec1 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_13 git_last_commit: 226a8df git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BEAT_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEAT_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEAT_1.30.0.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: 51 Package: BEclear Version: 2.8.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, outliers, abind, stats, graphics, utils, methods LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander License: GPL-3 Archs: i386, x64 MD5sum: a7430b0b90b803d0c12fcd1eec991333 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_13 git_last_commit: 820b246 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BEclear_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEclear_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEclear_2.8.0.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: 23 Package: BgeeCall Version: 1.8.0 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 MD5sum: ebe7acd141b9d860e7f75f9f219de889 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_13 git_last_commit: 75b35d9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BgeeCall_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BgeeCall_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BgeeCall_1.8.0.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: FALSE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 106 Package: BgeeDB Version: 2.18.1 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: 7470f5786c81d5b2d29e07ddf11e3c26 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_13 git_last_commit: 6e3f28d git_last_commit_date: 2021-06-15 Date/Publication: 2021-06-17 source.ver: src/contrib/BgeeDB_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BgeeDB_2.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BgeeDB_2.18.1.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 dependencyCount: 71 Package: BGmix Version: 1.52.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 9a4eaa30f39c26b86cd5f77f245672da 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_13 git_last_commit: ec212a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BGmix_1.52.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/BGmix_1.52.0.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: 1.58.0 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: i386, x64 MD5sum: 5c31fc7229f875092644d1d1c7554ede 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_13 git_last_commit: f9fb330 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bgx_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bgx_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bgx_1.58.0.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: 27 Package: BHC Version: 1.44.0 License: GPL-3 Archs: i386, x64 MD5sum: 530eae6ebaa22eeb651e1bdaa059d199 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_13 git_last_commit: 8a054fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BHC_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BHC_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BHC_1.44.0.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: 1.50.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: c7616f36acedf859f15ed54f4347bbc4 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_13 git_last_commit: 8c27ce5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BicARE_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BicARE_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BicARE_1.50.0.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: 58 Package: BiFET Version: 1.12.0 Imports: stats, poibin, GenomicRanges Suggests: testthat, knitr License: GPL-3 MD5sum: e69b752c9ef443517d51a1a1cb197d91 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_13 git_last_commit: 5eb0b62 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiFET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiFET_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiFET_1.12.0.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: 18 Package: BiGGR Version: 1.28.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: deae0796ed11ac9acaae0009b46f4292 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_13 git_last_commit: 1736ee9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiGGR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiGGR_1.28.0.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: 26 Package: bigmelon Version: 1.18.0 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 Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: a144656a74a4f28cc547fa9f0c8d477e 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_13 git_last_commit: 6ca2330 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bigmelon_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bigmelon_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bigmelon_1.18.0.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: 168 Package: bigPint Version: 1.8.0 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: fb78c1da2c94713869c60672cb19bb6d 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_13 git_last_commit: d2b72f4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bigPint_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bigPint_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bigPint_1.8.0.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: 125 Package: bioassayR Version: 1.30.0 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: 91fcdd20096a646e4aa7a1b4ab99804d 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_13 git_last_commit: 4c1993a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bioassayR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioassayR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bioassayR_1.30.0.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: 70 Package: Biobase Version: 2.52.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: f90f6de41c79d568600506b71b269961 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_13 git_last_commit: be5163c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Biobase_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Biobase_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Biobase_2.52.0.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, Autotuner, BAGS, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BioMVCClass, BioQC, biosigner, BLMA, 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, dualKS, dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, IVAS, lumi, macat, made4, mAPKL, massiR, MEAL, metagenomeSeq, metavizr, MethPed, methyAnalysis, 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dressCheck, etec16s, fabiaData, fibroEset, gaschYHS, golubEsets, GSE13015, GSE62944, GSVAdata, harbChIP, Hiiragi2013, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MAQCsubsetAFX, MAQCsubsetILM, MetaGxBreast, MetaGxOvarian, miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata, ProData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, pumadata, rcellminerData, RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA, tweeDEseqCountData, yeastCC, maEndToEnd, coreheat, countTransformers, crmn, dGAselID, GExMap, GWASbyCluster, heatmapFlex, lmQCM, MM2Sdata, MMDvariance, permGPU, propOverlap, statVisual importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, attract, ballgown, BASiCS, BayesKnockdown, biobroom, bioCancer, biocViews, BioNet, biscuiteer, BiSeq, blima, bnem, bsseq, BubbleTree, CAFE, canceR, Cardinal, CellScore, CellTrails, CGHnormaliter, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, coexnet, cogena, combi, conclus, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx, crlmm, crossmeta, ctgGEM, cummeRbund, cyanoFilter, cycle, cydar, CytoML, CytoTree, DAPAR, ddCt, debCAM, deco, DEGreport, DESeq2, DExMA, diffloop, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, FindMyFriends, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowStats, flowUtils, flowViz, flowWorkspace, FRASER, frma, frmaTools, GAPGOM, gCrisprTools, gcrma, GCSscore, genbankr, geneClassifiers, GeneExpressionSignature, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GenomicSuperSignature, GeomxTools, GEOsubmission, gep2pep, gespeR, ggbio, girafe, GISPA, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, imageHTS, ImmuneSpaceR, infinityFlow, IsoformSwitchAnalyzeR, IsoGeneGUI, isomiRs, iterClust, kissDE, lapmix, LiquidAssociation, LRBaseDbi, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MeSHDbi, metaseqR2, methyAnalysis, MethylAid, methylCC, methylumi, mfa, MiChip, microbiomeDASim, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, MMAPPR2, mogsa, MoonlightR, MOSim, MSEADbi, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, ontoProc, oposSOM, oppar, OrganismDbi, panp, phantasus, PharmacoGx, phemd, phyloseq, piano, plethy, plgem, plier, podkat, ppiStats, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, ProteomicsAnnotationHubData, PSEA, psygenet2r, ptairMS, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, RGalaxy, RIVER, Rmagpie, RNAinteract, rols, ROTS, RpsiXML, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, singscore, sitadela, SLGI, SomaticSignatures, spkTools, SPONGE, STATegRa, subSeq, TEQC, TFBSTools, timecourse, TMixClust, TnT, topdownr, ToxicoGx, tradeSeq, TTMap, twilight, uSORT, VanillaICE, 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, AnnoProbe, bapred, BisqueRNA, CDSeq, ClassComparison, ClassDiscovery, FMradio, geneExpressionFromGEO, HiResTEC, IntegratedJM, IsoGene, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, pulseTD, ragt2ridges, RobLox, RobLoxBioC, RPPanalyzer, ssizeRNA, TailRank suggestsMe: AUCell, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, CountClust, 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, POMA, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, slinky, 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, distrDoc, dnet, dplR, exp2flux, GenAlgo, hexbin, HTSCluster, isatabr, mi4p, Modeler, multiclassPairs, NACHO, optCluster, Patterns, pkgmaker, propr, rknn, Seurat, sigminer, SourceSet, tinyarray dependencyCount: 6 Package: biobroom Version: 1.24.0 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, SummarizedExperiment License: LGPL MD5sum: e162b0f55b1370aeb2bd2b42ec88da63 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_13 git_last_commit: 9a686b2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biobroom_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biobroom_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biobroom_1.24.0.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: 33 Package: biobtreeR Version: 1.4.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: bd43d4d10a308494f154dd80f4f4d61b 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_13 git_last_commit: f2a07b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biobtreeR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biobtreeR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biobtreeR_1.4.0.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: 1.20.02 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cgdsr(>= 1.2.6), XML(>= 3.98) Imports: 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: 7b622f955f654b54264a6051cb90b166 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_13 git_last_commit: 19fc7ba git_last_commit_date: 2021-06-30 Date/Publication: 2021-07-01 source.ver: src/contrib/bioCancer_1.20.02.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioCancer_1.20.02.zip mac.binary.ver: bin/macosx/contrib/4.1/bioCancer_1.20.02.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: 223 Package: BiocCheck Version: 1.28.0 Depends: R (>= 3.5.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, optparse, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, devtools (>= 1.4.1), usethis, BiocStyle Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: c8aeba2ae6d33edd8788083b478ba3d6 NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. 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/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_13 git_last_commit: 5fcd8f9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocCheck_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocCheck_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocCheck_1.28.0.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, scp, SpectralTAD, HMP16SData, HMP2Data, scpdata dependencyCount: 39 Package: BiocDockerManager Version: 1.4.0 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: 75557032482fd355b1987e45100f5bf5 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_13 git_last_commit: 16ea460 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocDockerManager_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocDockerManager_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocDockerManager_1.4.0.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: 46 Package: BiocFileCache Version: 2.0.0 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: cf1c1587fc63291045367246c98eba6e 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_13 git_last_commit: 280a8f9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocFileCache_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocFileCache_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocFileCache_2.0.0.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, TMExplorer, csawBook, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.workflows importsMe: AMARETTO, atSNP, autonomics, BayesSpace, BiocPkgTools, biomaRt, BrainSABER, brendaDb, cbaf, cBioPortalData, CellBench, conclus, CTDquerier, customCMPdb, dasper, easyRNASeq, EnrichmentBrowser, EpiTxDb, fgga, GAPGOM, GenomicScores, GenomicSuperSignature, GSEABenchmarkeR, gwascat, hca, MBQN, ontoProc, Organism.dplyr, psichomics, recount3, recountmethylation, regutools, RiboDiPA, rpx, SpatialExperiment, spatialHeatmap, TFutils, tximeta, UMI4Cats, UniProt.ws, waddR, org.Mxanthus.db, PANTHER.db, SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations suggestsMe: AnnotationForge, bambu, BiocOncoTK, BiocSet, HiCDCPlus, HumanTranscriptomeCompendium, MethReg, Nebulosa, progeny, seqsetvis, structToolbox, TCGAutils, TimeSeriesExperiment, zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData, msigdb, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, SingleRBook dependencyCount: 46 Package: BiocGenerics Version: 0.38.0 Depends: R (>= 4.0.0), methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 71dce70909c177f42bbd613d217304cd 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_13 git_last_commit: 1db849a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocGenerics_0.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocGenerics_0.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocGenerics_0.38.0.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, MeSHDbi, methyAnalysis, 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, TEQC, tigre, topdownr, topGO, UNDO, UniProt.ws, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss, liftOver, rsolr importsMe: a4Preproc, affycoretools, affylmGUI, AllelicImbalance, AneuFinder, annmap, annotate, AnnotationHubData, ArrayExpressHTS, ASpli, 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, contiBAIT, crlmm, crossmeta, csaw, ctgGEM, cummeRbund, cydar, dada2, dagLogo, DAMEfinder, ddCt, decompTumor2Sig, DEGreport, DelayedDataFrame, derfinder, DEScan2, DESeq2, DEWSeq, DEXSeq, diffcoexp, diffHic, DirichletMultinomial, DiscoRhythm, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisaR, enrichTF, epialleleR, epigenomix, EpiTxDb, epivizrChart, epivizrStandalone, erma, esATAC, FamAgg, fastseg, ffpe, FindMyFriends, 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, HiLDA, hiReadsProcessor, hopach, HTSeqGenie, icetea, igvR, IHW, IMAS, infercnv, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, IsoformSwitchAnalyzeR, isomiRs, IVAS, KCsmart, ldblock, lisaClust, LOLA, maser, MAST, matter, MEAL, meshr, metaMS, metaseqR2, methInheritSim, MethylAid, methylPipe, methylumi, mia, miaViz, mimager, MinimumDistance, MIRA, MiRaGE, missMethyl, MMAPPR2, Modstrings, mogsa, monocle, motifbreakR, msa, MSnID, MultiAssayExperiment, multicrispr, MultiDataSet, multiMiR, mumosa, MutationalPatterns, mzR, NanoStringNCTools, nearBynding, npGSEA, nucleR, oligoClasses, OmicsLonDA, openPrimeR, ORFik, OUTRIDER, parglms, pcaMethods, PDATK, pdInfoBuilder, PharmacoGx, phemd, PhIPData, PhosR, phyloseq, Pi, piano, PING, podkat, pram, primirTSS, proDA, profileScoreDist, pRoloc, pRolocGUI, PureCN, pwOmics, QFeatures, qPLEXanalyzer, qsea, QuasR, R3CPET, R453Plus1Toolbox, RaggedExperiment, ramr, ramwas, RCAS, RcisTarget, RCy3, RCyjs, recoup, REMP, ReportingTools, RGalaxy, RGMQL, RGSEA, 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, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, SLGI, SNPhood, snpStats, SpatialExperiment, Spectra, spicyR, splatter, SplicingGraphs, SQLDataFrame, sRACIPE, sscu, STAN, strandCheckR, Streamer, Structstrings, SummarizedExperiment, SynMut, TAPseq, target, TarSeqQC, TBSignatureProfiler, TCGAutils, TCseq, TFBSTools, TitanCNA, trackViewer, transcriptR, transite, TransView, TreeSummarizedExperiment, tRNA, tRNAdbImport, tRNAscanImport, TSRchitect, TVTB, 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, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, curatedCRCData, curatedOvarianData, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, scRNAseq, SingleCellMultiModal, systemPipeRdata, TENxBUSData, VariantToolsData, ExpHunterSuite, BinQuasi, crispRdesignR, DCLEAR, geno2proteo, MicroSEC, oncoPredict, PACVr, pagoo, pathwayTMB, RobLoxBioC, Signac, spectralAnalysis, utr.annotation suggestsMe: acde, aggregateBioVar, AIMS, ASSET, BaalChIP, baySeq, BDMMAcorrect, bigmelon, bigPint, BiocCheck, BiocParallel, BiocStyle, biocViews, BioMM, biosigner, BiRewire, BLMA, BloodGen3Module, bnem, BUScorrect, CAFE, CAMERA, CancerSubtypes, CAnD, CausalR, ccrepe, cellmigRation, ChIPanalyser, ChIPXpress, CHRONOS, CINdex, cleanUpdTSeq, clipper, clonotypeR, clustComp, CNORfeeder, CNORfuzzy, coexnet, coMET, consensus, cosmiq, COSNet, cpvSNP, CytoTree, DAPAR, DEsubs, DExMA, DMRcaller, DMRcate, EnhancedVolcano, ENmix, epiNEM, EventPointer, fCCAC, fcScan, fgga, FGNet, flowCL, flowCut, FlowRepositoryR, 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, hypergraph, iCARE, iClusterPlus, illuminaio, immunotation, InPAS, INPower, IPO, kebabs, KEGGREST, LACE, LRBaseDbi, MAGAR, mAPKL, massiR, MatrixQCvis, MatrixRider, MBttest, mCSEA, Mergeomics, Metab, MetaboSignal, metagene, metagene2, metagenomeSeq, MetCirc, methylCC, methylInheritance, MetNet, microbiome, miRBaseConverter, miRcomp, mirIntegrator, mnem, motifStack, MSEADbi, multiClust, MultiMed, multiOmicsViz, MungeSumstats, MWASTools, NBSplice, ncRNAtools, nempi, NetSAM, nondetects, nucleoSim, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, PathNet, 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MeSH.Ame.eg.db, MeSH.Aml.eg.db, MeSH.Ana.eg.db, MeSH.Ani.FGSC.eg.db, MeSH.AOR.db, MeSH.Ath.eg.db, MeSH.Bfl.eg.db, MeSH.Bsu.168.eg.db, MeSH.Bta.eg.db, MeSH.Cal.SC5314.eg.db, MeSH.Cbr.eg.db, MeSH.Cel.eg.db, MeSH.Cfa.eg.db, MeSH.Cin.eg.db, MeSH.Cja.eg.db, MeSH.Cpo.eg.db, MeSH.Cre.eg.db, MeSH.Dan.eg.db, MeSH.db, MeSH.Dda.3937.eg.db, MeSH.Ddi.AX4.eg.db, MeSH.Der.eg.db, MeSH.Dgr.eg.db, MeSH.Dme.eg.db, MeSH.Dmo.eg.db, MeSH.Dpe.eg.db, MeSH.Dre.eg.db, MeSH.Dse.eg.db, MeSH.Dsi.eg.db, MeSH.Dvi.eg.db, MeSH.Dya.eg.db, MeSH.Eca.eg.db, MeSH.Eco.K12.MG1655.eg.db, MeSH.Eco.O157.H7.Sakai.eg.db, MeSH.Gga.eg.db, MeSH.Gma.eg.db, MeSH.Hsa.eg.db, MeSH.Laf.eg.db, MeSH.Lma.eg.db, MeSH.Mdo.eg.db, MeSH.Mes.eg.db, MeSH.Mga.eg.db, MeSH.Miy.eg.db, MeSH.Mml.eg.db, MeSH.Mmu.eg.db, MeSH.Mtr.eg.db, MeSH.Nle.eg.db, MeSH.Oan.eg.db, MeSH.Ocu.eg.db, MeSH.Oni.eg.db, MeSH.Osa.eg.db, MeSH.Pab.eg.db, MeSH.Pae.PAO1.eg.db, MeSH.PCR.db, MeSH.Pfa.3D7.eg.db, MeSH.Pto.eg.db, MeSH.Ptr.eg.db, MeSH.Rno.eg.db, MeSH.Sce.S288c.eg.db, MeSH.Sco.A32.eg.db, MeSH.Sil.eg.db, MeSH.Spu.eg.db, MeSH.Ssc.eg.db, MeSH.Syn.eg.db, MeSH.Tbr.9274.eg.db, MeSH.Tgo.ME49.eg.db, MeSH.Tgu.eg.db, MeSH.Vvi.eg.db, MeSH.Xla.eg.db, MeSH.Xtr.eg.db, MeSH.Zma.eg.db, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, microRNAome, MIGSAdata, pwrEWAS.data, RegParallel, sesameData, adjclust, aroma.affymetrix, asteRisk, BioMedR, gkmSVM, MetaIntegrator, NutrienTrackeR, openSkies, pagoda2, polyRAD, Rediscover, Seurat dependencyCount: 5 Package: biocGraph Version: 1.54.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: d1632d5aa972bbd6c844a683cde39cd1 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_13 git_last_commit: f0f25d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biocGraph_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocGraph_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biocGraph_1.54.0.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: 55 Package: BiocIO Version: 1.2.0 Depends: R (>= 4.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: e2620b9925f7b088e833cec48beba281 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_13 git_last_commit: fef4c1c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocIO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocIO_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocIO_1.2.0.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: LoomExperiment importsMe: BiocSet, GenomicFeatures, rtracklayer dependencyCount: 9 Package: BiocNeighbors Version: 1.10.0 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: i386, x64 MD5sum: 3c33f083803675ee300ec9c1a8ab04ed 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_13 git_last_commit: c6779d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocNeighbors_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocNeighbors_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocNeighbors_1.10.0.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, miloR, mumosa, scater, scDblFinder, SingleR suggestsMe: TrajectoryUtils, TSCAN, SingleRBook dependencyCount: 21 Package: BiocOncoTK Version: 1.12.1 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: 750e34afe2350bb4c947a8527bbba1fc 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_13 git_last_commit: 5b4a389 git_last_commit_date: 2021-06-28 Date/Publication: 2021-06-29 source.ver: src/contrib/BiocOncoTK_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocOncoTK_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocOncoTK_1.12.1.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: 207 Package: BioCor Version: 1.16.0 Depends: R (>= 3.4.0) Imports: BiocParallel, Matrix, methods, GSEABase Suggests: reactome.db, org.Hs.eg.db, WGCNA, GOSemSim, testthat, knitr, rmarkdown, BiocStyle, airway, DESeq2, boot, targetscan.Hs.eg.db, Hmisc, spelling License: MIT + file LICENSE MD5sum: b51e934fb7218761bd21f61ab9178105 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://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_13 git_last_commit: 8b830d8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioCor_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioCor_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioCor_1.16.0.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: 61 Package: BiocParallel Version: 1.26.2 Depends: methods Imports: stats, utils, futile.logger, parallel, snow LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 88046f3400849338d9db337984586099 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: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb] Maintainer: Bioconductor Package Maintainer 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_13 git_last_commit: e6f7d36 git_last_commit_date: 2021-08-19 Date/Publication: 2021-08-22 source.ver: src/contrib/BiocParallel_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocParallel_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocParallel_1.26.2.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 vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to 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 dependsOnMe: bacon, BEclear, Cardinal, ClassifyR, clusterSeq, consensusSeekeR, CopywriteR, deco, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, FEAST, FRASER, GenomicFiles, hiReadsProcessor, INSPEcT, matter, MBASED, metagene, metagene2, 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, ALPS, AlpsNMR, amplican, ASICS, ASpediaFI, atSNP, bambu, BANDITS, BASiCS, batchelor, bayNorm, BiocNeighbors, BioCor, BiocSingular, BioMM, BioNERO, BioNetStat, biotmle, biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, cellbaseR, CellBench, CelliD, CellMixS, censcyt, ChIPexoQual, ChIPQC, ChromSCape, chromswitch, chromVAR, CNVRanger, CoGAPS, condiments, consensusDE, contiBAIT, CoreGx, coseq, cpvSNP, CRISPRseek, CrispRVariants, csaw, cydar, CytoGLMM, cytomapper, dasper, dcGSA, debCAM, DEComplexDisease, derfinder, DEScan2, DESeq2, DEsingle, DiffBind, dmrseq, DOSE, DRIMSeq, DropletUtils, Dune, easyRNASeq, EMDomics, erma, ERSSA, escape, fgsea, FindMyFriends, flowcatchR, flowSpecs, GDCRNATools, GenoGAM, GenomicAlignments, genotypeeval, gmapR, gscreend, GSEABenchmarkeR, GSVA, GUIDEseq, h5vc, HiCBricks, HiCcompare, HTSeqGenie, HTSFilter, iasva, icetea, ideal, IMAS, InPAS, IntEREst, IONiseR, IPO, ISAnalytics, KinSwingR, LineagePulse, lisaClust, loci2path, LowMACA, LRcell, MACPET, mbkmeans, MCbiclust, metabomxtr, metaseqR2, MethCP, MethylAid, methylGSA, methylInheritance, methylscaper, MetNet, mia, miaViz, MIGSA, miloR, minfi, miQC, mixOmics, MMAPPR2, MOGAMUN, motifbreakR, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MSnbase, msqrob2, MSstatsSampleSize, multiHiCcompare, mumosa, muscat, NBAMSeq, NBSplice, NPARC, OmicsLonDA, ORFik, OVESEG, PAIRADISE, PCAtools, PDATK, PharmacoGx, pipeComp, pram, PrecisionTrialDrawer, proActiv, proFIA, profileplyr, qpgraph, qsea, QuasR, RadioGx, Rcwl, recount, RegEnrich, REMP, RJMCMCNucleosomes, RNAmodR, Rsamtools, RUVcorr, satuRn, scater, scClassify, scDblFinder, scDD, scde, SCFA, scHOT, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, scruff, scTHI, scuttle, sesame, SEtools, sigFeature, signatureSearch, singleCellTK, SingleR, singscore, SNPhood, soGGi, SpectralTAD, spicyR, splatter, SplicingGraphs, srnadiff, TAPseq, TarSeqQC, TBSignatureProfiler, ternarynet, TFBSTools, TMixClust, ToxicoGx, TPP2D, tradeSeq, TraRe, TreeSummarizedExperiment, trena, Trendy, TSRchitect, TVTB, VariantFiltering, VariantTools, velociraptor, waddR, weitrix, zinbwave, IHWpaper, ExpHunterSuite, DCLEAR, DysPIA, enviGCMS, minSNPs suggestsMe: beachmat, DelayedArray, DIAlignR, glmGamPoi, HDF5Array, netSmooth, omicsPrint, PureCN, randRotation, RcisTarget, rebook, scGPS, SeqArray, systemPipeR, TFutils, TileDBArray, tofsims, TrajectoryUtils, TSCAN, universalmotif, MethylAidData, TENxBrainData, TENxPBMCData, CAGEWorkflow, SingleRBook, conos, Corbi, digitalDLSorteR, pagoda2, phase1RMD, survBootOutliers, wrTopDownFrag dependencyCount: 10 Package: BiocPkgTools Version: 1.10.2 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: 96ad2d065ba2042bfea23156ffc6a3af 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], 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_13 git_last_commit: eed4730 git_last_commit_date: 2021-09-16 Date/Publication: 2021-09-19 source.ver: src/contrib/BiocPkgTools_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocPkgTools_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocPkgTools_1.10.2.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: 92 Package: BiocSet Version: 1.6.1 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: 44faf6862e70268f0b3a471c606d9010 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_13 git_last_commit: 9572684 git_last_commit_date: 2021-08-05 Date/Publication: 2021-08-08 source.ver: src/contrib/BiocSet_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSet_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSet_1.6.1.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 suggestsMe: dearseq dependencyCount: 62 Package: BiocSingular Version: 1.8.1 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: i386, x64 MD5sum: e5f008179a2f3941cb13a2aa00210c45 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_13 git_last_commit: 860033b git_last_commit_date: 2021-06-08 Date/Publication: 2021-06-08 source.ver: src/contrib/BiocSingular_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSingular_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSingular_1.8.1.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, GSVA, miloR, mumosa, NewWave, PCAtools, scater, scDblFinder, scMerge, scran, scry, SingleR, velociraptor suggestsMe: ResidualMatrix, ScaledMatrix, splatter, HCAData dependencyCount: 28 Package: BiocSklearn Version: 1.14.1 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment, knitr Imports: basilisk, Rcpp Suggests: testthat, restfulSE, HDF5Array, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: fcb7c754080f294dfa0d93c650a33f1d 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_13 git_last_commit: df73d5c git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/BiocSklearn_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSklearn_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSklearn_1.14.1.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: 47 Package: BiocStyle Version: 2.20.2 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: 782fb5e050ffc389db227977f2aa57fd 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_13 git_last_commit: a35cffe git_last_commit_date: 2021-06-17 Date/Publication: 2021-06-17 source.ver: src/contrib/BiocStyle_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocStyle_2.20.2.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocStyle_2.20.2.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: 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ramwas, RandomWalkRestartMH, randRotation, rawrr, Rbowtie, Rcade, rcellminer, rCGH, RcisTarget, Rcwl, RcwlPipelines, RCy3, RCyjs, ReactomePA, recount, recount3, recountmethylation, recoup, RedeR, regioneR, regsplice, regutools, ReQON, ResidualMatrix, restfulSE, rexposome, rfaRm, Rfastp, rfPred, RGMQL, RGraph2js, RGSEA, rhdf5, rhdf5client, rhdf5filters, Rhdf5lib, Rhisat2, Rhtslib, RiboProfiling, riboSeqR, ribosomeProfilingQC, RIVER, RJMCMCNucleosomes, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, rnaseqcomp, RnaSeqSampleSize, Rnits, ROCpAI, rols, ropls, rpx, rqt, rrvgo, Rsamtools, rScudo, rsemmed, rSWeeP, RTCGAToolbox, RTN, RTNduals, RTNsurvival, Rtpca, RUVSeq, RVS, rWikiPathways, S4Vectors, sampleClassifier, sangerseqR, satuRn, ScaledMatrix, scater, scCB2, scClassify, scDblFinder, scDD, scds, scFeatureFilter, scMerge, SCnorm, scone, scoreInvHap, scp, scPCA, scran, scRepertoire, scruff, scuttle, sechm, segmentSeq, selectKSigs, seqCAT, SeqGate, seqLogo, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenC, SGSeq, SharedObject, shinyMethyl, ShortRead, SIAMCAT, SigCheck, SigFuge, signatureSearch, SigsPack, SIMD, SimFFPE, similaRpeak, SIMLR, sincell, SingleCellExperiment, singleCellTK, SingleR, sitePath, slingshot, slinky, SMAD, snapcount, snifter, SNPediaR, SNPhood, soGGi, sojourner, SOMNiBUS, sparseDOSSA, sparseMatrixStats, sparsenetgls, SparseSignatures, SpatialCPie, SpatialExperiment, spatialHeatmap, specL, Spectra, SpectralTAD, spicyR, SpidermiR, splatter, SPLINTER, splots, spqn, SPsimSeq, sRACIPE, srnadiff, stageR, STAN, StarBioTrek, STATegRa, statTarget, strandCheckR, struct, Structstrings, structToolbox, SubCellBarCode, SummarizedBenchmark, SummarizedExperiment, sva, swfdr, switchde, SynExtend, systemPipeR, systemPipeShiny, systemPipeTools, TADCompare, TargetSearch, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TFARM, TFBSTools, TFHAZ, TFutils, tidybulk, tidySingleCellExperiment, tidySummarizedExperiment, tigre, TileDBArray, timeOmics, TMixClust, TOAST, tomoda, topconfects, topdownr, ToxicoGx, TPP, tracktables, trackViewer, TrajectoryUtils, transcriptogramer, transcriptR, transomics2cytoscape, TraRe, Travel, TreeAndLeaf, treekoR, TreeSummarizedExperiment, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TRONCO, TTMap, TurboNorm, TVTB, twoddpcr, Ularcirc, UMI4Cats, uncoverappLib, UniProt.ws, variancePartition, VariantAnnotation, VariantFiltering, VCFArray, velociraptor, VERSO, vidger, ViSEAGO, vissE, vsn, wavClusteR, weitrix, wpm, xcms, Xeva, XNAString, yamss, YAPSA, zellkonverter, zinbwave, AHEnsDbs, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, hpAnnot, rat2302frmavecs, ABAData, ASICSdata, AssessORFData, benchmarkfdrData2019, BioImageDbs, blimaTestingData, BloodCancerMultiOmics2017, bodymapRat, CardinalWorkflows, celldex, CellMapperData, chipenrich.data, ChIPexoQualExample, chipseqDBData, CLLmethylation, clustifyrdatahub, CopyhelpeR, COSMIC.67, curatedBladderData, curatedCRCData, curatedMetagenomicData, curatedOvarianData, curatedTCGAData, depmap, derfinderData, DExMAdata, DmelSGI, dorothea, DropletTestFiles, DuoClustering2018, ELMER.data, emtdata, ewceData, furrowSeg, GenomicDistributionsData, GeuvadisTranscriptExpr, GSE13015, GSE62944, HarmanData, HCAData, HD2013SGI, HDCytoData, HelloRangesData, HighlyReplicatedRNASeq, Hiiragi2013, HMP16SData, HMP2Data, HumanAffyData, IHWpaper, imcdatasets, LRcellTypeMarkers, mCSEAdata, MetaGxPancreas, MethylAidData, MethylSeqData, microbiomeDataSets, minionSummaryData, MMAPPR2data, MouseGastrulationData, MouseThymusAgeing, msigdb, MSMB, msqc1, MSstatsBioData, muscData, nanotubes, NestLink, OnassisJavaLibs, optimalFlowData, parathyroidSE, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, preciseTADhub, ptairData, rcellminerData, RforProteomics, RGMQLlib, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, SCLCBam, scpdata, scRNAseq, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, spatialLIBD, STexampleData, systemPipeRdata, TabulaMurisData, tartare, TCGAbiolinksGUI.data, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, VariantToolsData, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, proteomics, recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations, variants, SingleRBook, asteRisk, BOSO, EHRtemporalVariability, ggBubbles, i2dash, magmaR, MetaIntegrator, multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, SourceSet dependencyCount: 25 Package: biocthis Version: 1.2.0 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: 91e17ef553b75239691c002bdf7a6885 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_13 git_last_commit: bc58cac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biocthis_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocthis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biocthis_1.2.0.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 dependencyCount: 54 Package: BiocVersion Version: 3.13.1 Depends: R (>= 4.1.0) License: Artistic-2.0 MD5sum: 4a15cac357ba19fc81e6508083cdddca 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: 3466413 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/BiocVersion_3.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocVersion_3.13.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocVersion_3.13.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.60.0 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: 9eff6cb8ccbee3089a1645a1ae47198a 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_13 git_last_commit: b861d8d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biocViews_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocViews_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biocViews_1.60.0.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: 17 Package: BiocWorkflowTools Version: 1.18.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 3d55e45e9636b702ef648e68d1e5d8a8 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_13 git_last_commit: 3894fab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocWorkflowTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocWorkflowTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocWorkflowTools_1.18.0.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: 54 Package: biodb Version: 1.0.4 Depends: R (>= 4.0) Imports: R6, methods, chk, lgr, progress, lifecycle, XML, stringr, plyr, yaml, jsonlite, RCurl, Rcpp, rappdirs, stats, openssl, RSQLite, withr LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, xml2, git2r License: AGPL-3 Archs: i386, x64 MD5sum: f08973024771b262763c2b905017dd95 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodb git_branch: RELEASE_3_13 git_last_commit: 84b7aff git_last_commit_date: 2021-06-09 Date/Publication: 2021-06-10 source.ver: src/contrib/biodb_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodb_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.1/biodb_1.0.4.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html, vignettes/biodb/inst/doc/in_house_compound_db.html, vignettes/biodb/inst/doc/in_house_mass_db.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, In-house compound database, In-house LCMS database, 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/in_house_compound_db.R, vignettes/biodb/inst/doc/in_house_mass_db.R, vignettes/biodb/inst/doc/new_connector.R, vignettes/biodb/inst/doc/new_entry_field.R dependencyCount: 63 Package: bioDist Version: 1.64.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 4f0d06bf8e817ec68bc2f60f0625ab47 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_13 git_last_commit: acf9621 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bioDist_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioDist_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bioDist_1.64.0.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: 8 Package: biomaRt Version: 2.48.3 Depends: methods Imports: utils, XML, AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 Suggests: BiocStyle, knitr, rmarkdown, testthat, mockery License: Artistic-2.0 MD5sum: e1b8dd1277db4a59c93dd744dab43053 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_13 git_last_commit: 66a592e git_last_commit_date: 2021-08-12 Date/Publication: 2021-08-15 source.ver: src/contrib/biomaRt_2.48.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomaRt_2.48.3.zip mac.binary.ver: bin/macosx/contrib/4.1/biomaRt_2.48.3.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, cosmosR, cTRAP, dagLogo, DEXSeq, diffloop, DominoEffect, easyRNASeq, EDASeq, ELMER, EWCE, FRASER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, gpart, Gviz, InterCellar, isobar, mCSEA, MEDIPS, MetaboSignal, metaseqR2, methyAnalysis, MGFR, OncoScore, oposSOM, pcaExplorer, phenoTest, PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, scPipe, seq2pathway, SeqGSEA, sitadela, SPLINTER, SWATH2stats, TCGAbiolinks, TFEA.ChIP, TimiRGeN, transcriptogramer, trena, ViSEAGO, XCIR, yarn, ExpHunterSuite, TCGAWorkflow, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, intePareto, kangar00, liayson, snplist, utr.annotation suggestsMe: AnnotationForge, bioassayR, celda, cellTree, chromstaR, ClusterJudge, CNVgears, ctgGEM, fedup, FELLA, 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, systemPipeR, trackViewer, wiggleplotr, zinbwave, BloodCancerMultiOmics2017, ccTutorial, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, cinaR, DGEobj, DGEobj.utils, loose.rock, Patterns, R.SamBada, scDiffCom, SNPassoc dependencyCount: 71 Package: biomformat Version: 1.20.0 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: 571c3ec839e5519bb8a2f0d63ed92010 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_13 git_last_commit: 986d2aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biomformat_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomformat_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biomformat_1.20.0.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, phyloseq suggestsMe: metagenomeSeq, mia, MicrobiotaProcess, metacoder, PLNmodels dependencyCount: 14 Package: BioMM Version: 1.8.0 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: 0d0d957d7bb3d3c7695a6917202edc56 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_13 git_last_commit: 765ffb7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioMM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioMM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioMM_1.8.0.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: 147 Package: BioMVCClass Version: 1.60.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 762b576b94002464c02379783982ee34 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_13 git_last_commit: b626fb8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioMVCClass_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioMVCClass_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioMVCClass_1.60.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.32.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: b7240d55047faf5c5f54b1cf91a2848a 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_13 git_last_commit: c4c6948 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biomvRCNS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomvRCNS_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biomvRCNS_1.32.0.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: 143 Package: BioNERO Version: 1.0.4 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, matrixStats, DESeq2, sva, RColorBrewer, ComplexHeatmap, ggplot2, reshape2, igraph, ggnetwork, intergraph, networkD3, ggnewscale, ggpubr, NetRep, stats, grDevices, graphics, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, covr License: GPL-3 MD5sum: c94eae1c36051e89beceafd39bfdf3bc 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_13 git_last_commit: 31c35e0 git_last_commit_date: 2021-08-26 Date/Publication: 2021-08-29 source.ver: src/contrib/BioNERO_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNERO_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNERO_1.0.4.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 dependencyCount: 202 Package: BioNet Version: 1.52.0 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: e2fdccded3c80cd3729b4725bec71828 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_13 git_last_commit: 5045304 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioNet_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNet_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNet_1.52.0.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 dependencyCount: 54 Package: BioNetStat Version: 1.12.0 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: 87e7808be0fce32784c99f576350446e 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_13 git_last_commit: 1bd4d5e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioNetStat_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNetStat_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNetStat_1.12.0.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: 139 Package: BioQC Version: 1.20.0 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: i386, x64 MD5sum: 946a03e11707bf2bcf4351a3390b09f9 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_13 git_last_commit: 1f94f0f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioQC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioQC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioQC_1.20.0.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: 14 Package: biosigner Version: 1.20.0 Depends: Biobase, ropls Imports: methods, e1071, MultiDataSet, randomForest Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 7aabe07f9ac432b7173f3c3444296270 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 Author: Philippe Rinaudo , Etienne Thevenot Maintainer: Philippe Rinaudo , Etienne Thevenot VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_13 git_last_commit: 2186320 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biosigner_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biosigner_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biosigner_1.20.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R importsMe: multiSight dependencyCount: 67 Package: Biostrings Version: 2.60.2 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), 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: i386, x64 MD5sum: 7fe01dff7e42438da469b9bfac1a5824 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_13 git_last_commit: 2024073 git_last_commit_date: 2021-08-04 Date/Publication: 2021-08-05 source.ver: src/contrib/Biostrings_2.60.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/Biostrings_2.60.2.zip mac.binary.ver: bin/macosx/contrib/4.1/Biostrings_2.60.2.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, Structstrings, 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, 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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, ActiveDriverWGS, alakazam, BALCONY, BASiNET, biomartr, BioMedR, crispRdesignR, CSESA, deepredeff, dowser, EncDNA, ensembleTax, ExomeDepth, genBaRcode, hoardeR, ICAMS, immuneSIM, kibior, metaCluster, microbial, MicroSEC, PACVr, PredCRG, ptm, RAPIDR, seqmagick, simMP, SMITIDstruct, utr.annotation, vhcub suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS, BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles, GenomicRanges, GWASTools, maftools, methrix, methylumi, MiRaGE, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, splatter, systemPipeTools, treeio, 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, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, apcluster, bbl, bio3d, DDPNA, file2meco, gkmSVM, maGUI, minSNPs, msaR, NameNeedle, phangorn, polyRAD, protr, rDNAse, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 18 Package: BioTIP Version: 1.6.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, Hmisc, MASS Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 4e8af952bf601f1b187eff5dd0d11b3b 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_13 git_last_commit: 42fb23f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BioTIP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioTIP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioTIP_1.6.0.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: 84 Package: biotmle Version: 1.16.0 Depends: R (>= 3.4) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, future, doFuture, 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: file LICENSE MD5sum: fde0cc24d59a2ed6288edf0abdcdd52b 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] () 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_13 git_last_commit: 64f9cfb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biotmle_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biotmle_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biotmle_1.16.0.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: 103 Package: biovizBase Version: 1.40.0 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: i386, x64 MD5sum: 83ad0eb484435c661f92f3fc661ed4f5 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_13 git_last_commit: e741735 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biovizBase_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biovizBase_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biovizBase_1.40.0.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: 140 Package: BiRewire Version: 3.24.0 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 6ade1bd63fcd516a946b71888bfec2f9 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). 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_13 git_last_commit: 6a27de6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiRewire_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiRewire_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiRewire_3.24.0.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 importsMe: NetSci dependencyCount: 13 Package: biscuiteer Version: 1.6.0 Depends: R (>= 3.6), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10 License: GPL-3 MD5sum: 2d9427247e55a8a4733b6e28e576629b 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, Jr. [aut, cre], Wanding Zhou [aut], Ben Johnson [aut], Jacob Morrison [aut], Lyong Heo [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_13 git_last_commit: 6d86b7b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/biscuiteer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biscuiteer_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biscuiteer_1.6.0.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: 1.32.0 Depends: R (>= 2.15.2), 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: 860251482e8b71a756166beabb36066a 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_13 git_last_commit: d5b9175 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiSeq_1.32.0.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: 85 Package: BitSeq Version: 1.36.0 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges, methods, utils LinkingTo: Rhtslib (>= 1.15.5) Suggests: BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 064f5a2290153aab6119a09fa150fe76 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_13 git_last_commit: 9a22089 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BitSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BitSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BitSeq_1.36.0.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: 29 Package: blacksheepr Version: 1.6.0 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: 2924fa0deced5f622dd30136e0e39bd1 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_13 git_last_commit: 665a6ef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/blacksheepr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/blacksheepr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/blacksheepr_1.6.0.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: 73 Package: blima Version: 1.26.0 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: i386, x64 MD5sum: cbd6964c97503a615dc5c40bfdbf207d 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_13 git_last_commit: f56780e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/blima_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/blima_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/blima_1.26.0.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: 83 Package: BLMA Version: 1.16.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 758c53e5198d2f77d77318ebcc4be708 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_13 git_last_commit: 9b4e0e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BLMA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BLMA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BLMA_1.16.0.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: 72 Package: BloodGen3Module Version: 1.0.0 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: 659d692fe121a5ec79e5ae4b70b6176e 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_13 git_last_commit: 2f62a72 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BloodGen3Module_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BloodGen3Module_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BloodGen3Module_1.0.0.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: 147 Package: bluster Version: 1.2.1 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: i386, x64 MD5sum: 9704c99eda7a3abd6d647b4df013eda9 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_13 git_last_commit: 5657043 git_last_commit_date: 2021-05-26 Date/Publication: 2021-05-27 source.ver: src/contrib/bluster_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/bluster_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/bluster_1.2.1.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 suggestsMe: batchelor, dittoSeq, mbkmeans, mumosa, SingleRBook dependencyCount: 26 Package: bnbc Version: 1.14.0 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: i386, x64 MD5sum: da92967d20be9183289894f808c13257 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_13 git_last_commit: 210c4d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bnbc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bnbc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bnbc_1.14.0.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: 88 Package: bnem Version: 1.0.0 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: 799b749b59a30d2175b816f10ee1c50f 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_13 git_last_commit: 3109efd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bnem_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bnem_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bnem_1.0.0.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: 167 Package: BPRMeth Version: 1.18.0 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: i386, x64 MD5sum: 7e75c0c13f889e7cee2b04ff7aa82aa8 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_13 git_last_commit: 2d62182 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BPRMeth_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BPRMeth_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BPRMeth_1.18.0.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: 90 Package: BRAIN Version: 1.38.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 363e4448d977951768c1352cb9c52cef 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_13 git_last_commit: eea0fc0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BRAIN_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BRAIN_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BRAIN_1.38.0.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, RforProteomics dependencyCount: 23 Package: brainflowprobes Version: 1.6.0 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: 2593847a9e05c50efd0bb9eff97c586d 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_13 git_last_commit: 3bae675 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/brainflowprobes_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/brainflowprobes_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/brainflowprobes_1.6.0.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: 187 Package: BrainSABER Version: 1.2.0 Depends: R (>= 4.0), biomaRt, SummarizedExperiment Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr, plotly License: Artistic-2.0 MD5sum: c6dfd33ab7845bec573782837f3b042d 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 and Evgeni Radichev Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BrainSABER git_branch: RELEASE_3_13 git_last_commit: 0c1b22d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BrainSABER_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BrainSABER_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BrainSABER_1.2.0.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: 83 Package: branchpointer Version: 1.18.0 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: 523c92c1f991aca3e1381dacca600018 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_13 git_last_commit: 0e0e947 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/branchpointer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/branchpointer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/branchpointer_1.18.0.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: 147 Package: breakpointR Version: 1.10.0 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: 6b5ba34d042d0139adcc8a59576b75b7 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_13 git_last_commit: c1d2077 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/breakpointR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/breakpointR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/breakpointR_1.10.0.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: 74 Package: brendaDb Version: 1.6.0 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 MD5sum: 681ff31d687d330e1ef168ce234e09a5 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_13 git_last_commit: afc5e0a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/brendaDb_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/brendaDb_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/brendaDb_1.6.0.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: 60 Package: BRGenomics Version: 1.4.0 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 Archs: i386, x64 MD5sum: 90909e392a5ced0c93fcaed7b527b3e3 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_13 git_last_commit: b006074 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BRGenomics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BRGenomics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BRGenomics_1.4.0.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 dependencyCount: 101 Package: bridge Version: 1.56.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) MD5sum: 455738fee88083a39b6881c1e6191c6b 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_13 git_last_commit: f04b4aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bridge_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bridge_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bridge_1.56.0.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: 2.2.1 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 Archs: i386, x64 MD5sum: 4bcfb4daf5033d9c0224f38445e73c6e 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_13 git_last_commit: c49fd62 git_last_commit_date: 2021-04-26 Date/Publication: 2021-05-25 source.ver: src/contrib/BridgeDbR_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BridgeDbR_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BridgeDbR_2.2.1.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: 2.14.1 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 72c590ceea1534446a59a3b33b9395e0 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_13 git_last_commit: 52e8d96 git_last_commit_date: 2021-09-14 Date/Publication: 2021-09-16 source.ver: src/contrib/BrowserViz_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BrowserViz_2.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BrowserViz_2.14.1.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: 14 Package: BSgenome Version: 1.60.0 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: 1242d18c41a3d3d709938179e561ba85 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_13 git_last_commit: 6643064 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BSgenome_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BSgenome_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BSgenome_1.60.0.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: 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.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.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.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, 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.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.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, 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, CRISPRseek, crisprseekplus, diffHic, dpeak, enrichTF, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR, MADSEQ, methrix, MethylSeekR, MMDiff2, motifbreakR, motifmatchr, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP, Repitools, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, SigsPack, SingleMoleculeFootprinting, SparseSignatures, 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.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.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.Hsapiens.NCBI.GRCh38, 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.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.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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.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, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ActiveDriverWGS, deconstructSigs, ICAMS, simMP suggestsMe: bambu, Biostrings, biovizBase, ChIPpeakAnno, chipseq, easyRNASeq, eisaR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, PWMEnrich, QDNAseq, recoup, rtracklayer, sitadela, SNPlocs.Hsapiens.dbSNP.20101109, gkmSVM, sigminer, Signac dependencyCount: 44 Package: bsseq Version: 1.28.0 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 MD5sum: 29efb76e1e573f8748f4bd357a1308cd 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_13 git_last_commit: 57084fa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bsseq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bsseq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bsseq_1.28.0.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: DMRcate, MethCP, methylCC, methylSig, MIRA, NanoMethViz, scmeth, tcgaWGBSData.hg19 suggestsMe: methrix, tissueTreg dependencyCount: 72 Package: BubbleTree Version: 2.22.0 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) Archs: i386, x64 MD5sum: 6ef3a6161050c9136bf40f3e29f8ae66 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_13 git_last_commit: d94bc48 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BubbleTree_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BubbleTree_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BubbleTree_2.22.0.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: 154 Package: BufferedMatrix Version: 1.56.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) MD5sum: a135b71cb4866118fdc79effbfb94d8b 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_13 git_last_commit: 64ce6a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BufferedMatrix_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BufferedMatrix_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrix_1.56.0.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: 1.56.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: 86cb4bffae827ad2c6753c978acecf0f 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_13 git_last_commit: e312294 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BufferedMatrixMethods_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BufferedMatrixMethods_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrixMethods_1.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: BUMHMM Version: 1.16.0 Depends: R (>= 3.4) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: ff6209c8680402aa8564ec0449ee2995 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_13 git_last_commit: 96ebaad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BUMHMM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUMHMM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUMHMM_1.16.0.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: 99 Package: bumphunter Version: 1.34.0 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: 50b1b1a560f8a69b5cd3afd14a8466c9 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [cre, 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] Maintainer: Rafael A. Irizarry URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_13 git_last_commit: 905ec98 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/bumphunter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bumphunter_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bumphunter_1.34.0.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, DAMEfinder, derfinder, dmrseq, epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 103 Package: BumpyMatrix Version: 1.0.1 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 1eb71c420fb9ac68e0ea45c38518dc39 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_13 git_last_commit: 24a8f24 git_last_commit_date: 2021-07-03 Date/Publication: 2021-07-04 source.ver: src/contrib/BumpyMatrix_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BumpyMatrix_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BumpyMatrix_1.0.1.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: MouseGastrulationData suggestsMe: SpatialExperiment dependencyCount: 13 Package: BUS Version: 1.48.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 MD5sum: fcf872999125cf981e9112953fc1d1a0 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_13 git_last_commit: 4069c62 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BUS_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUS_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUS_1.48.0.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: 1.10.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 769fd235046b9b7840ab1ce165099982 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_13 git_last_commit: 023b2d8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BUScorrect_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUScorrect_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUScorrect_1.10.0.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: 30 Package: BUSpaRse Version: 1.6.1 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: i386, x64 MD5sum: a82009b4f7d9aa56416fb5017e081019 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_13 git_last_commit: 10db5d4 git_last_commit_date: 2021-06-07 Date/Publication: 2021-06-08 source.ver: src/contrib/BUSpaRse_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUSpaRse_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BUSpaRse_1.6.1.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: 121 Package: CAEN Version: 1.0.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 7edb7daa0585d3aea9201e8c6dc4a349 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_13 git_last_commit: da2a7b2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAEN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAEN_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAEN_1.0.0.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: 27 Package: CAFE Version: 1.28.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 4bb462d696b914d4fe34d26c6de8a6c0 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_13 git_last_commit: 29db053 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAFE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAFE_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAFE_1.28.0.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: 159 Package: CAGEfightR Version: 1.12.0 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: ff430bb22c862b879df3e6dc267cdb40 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_13 git_last_commit: f776ea4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAGEfightR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAGEfightR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAGEfightR_1.12.0.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: 149 Package: CAGEr Version: 1.34.0 Depends: methods, MultiAssayExperiment, R (>= 3.5.0) Imports: beanplot, BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape, 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: 2f1b075e0872267fc821a90189a3f5f0 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_13 git_last_commit: 8e7ccda git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAGEr_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAGEr_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAGEr_1.34.0.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: 100 Package: calm Version: 1.6.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: b076f5c4ecb6b8f768816776c22bb254 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_13 git_last_commit: e837e6d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/calm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/calm_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/calm_1.6.0.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: 1.48.0 Depends: R (>= 2.1.0), 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) MD5sum: 7b6e0ef39f4c9312bd0ebf80a2d5adef 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_13 git_last_commit: 5ad3135 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAMERA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAMERA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAMERA_1.48.0.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: 125 Package: canceR Version: 1.26.0 Depends: R (>= 3.4), tcltk, tcltk2, cgdsr Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices Suggests: testthat (>= 0.10.0), R.rsp License: GPL-2 Archs: i386, x64 MD5sum: 3c72ad1b32616cce68a2374ed310db4d 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, Software Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_13 git_last_commit: 317c568 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/canceR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/canceR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/canceR_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 158 Package: cancerclass Version: 1.36.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 MD5sum: 8359a248c09c9c94e4c33824ddf1fa57 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_13 git_last_commit: d4617dd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cancerclass_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cancerclass_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cancerclass_1.36.0.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: 8 Package: CancerInSilico Version: 2.12.0 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: i386, x64 MD5sum: 7d3c55254bc70d782faf5f0003b14052 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_13 git_last_commit: 1336eb2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CancerInSilico_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CancerInSilico_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CancerInSilico_2.12.0.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: 1.18.0 Depends: R (>= 4.0), sigclust, NMF Imports: iCluster, cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, knitr, RTCGA.mRNA, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 0b28da8a1ba2739fb9f2e7b909336877 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_13 git_last_commit: 66e771e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CancerSubtypes_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CancerSubtypes_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CancerSubtypes_1.18.0.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: 73 Package: CAnD Version: 1.24.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 039660ce0f855831098ccd6a52d9ddb1 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: RELEASE_3_13 git_last_commit: c6d5434 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CAnD_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAnD_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAnD_1.24.0.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: 41 Package: caOmicsV Version: 1.22.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: 2b9795e0e0679e633fa36635013625e7 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: RELEASE_3_13 git_last_commit: a570ed1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/caOmicsV_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/caOmicsV_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/caOmicsV_1.22.0.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: 14 Package: Cardinal Version: 2.10.0 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 MD5sum: 64fd3b7762ffbf42555e744772e4e480 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_13 git_last_commit: 174f14c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Cardinal_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cardinal_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cardinal_2.10.0.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: 65 Package: CARNIVAL Version: 2.2.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, rjson, rmarkdown, methods Suggests: knitr, testthat (>= 2.1.0) License: GPL-3 Archs: i386, x64 MD5sum: f46bc9fee4e23e78b6e97c863cdca7b8 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], Olga Ivanova [cre] Maintainer: Olga Ivanova 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_13 git_last_commit: 06a5d06 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CARNIVAL_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CARNIVAL_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CARNIVAL_2.2.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: narray Usage Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR dependencyCount: 56 Package: casper Version: 2.26.0 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) MD5sum: 2f4e512c301672e60ab3d0781a69305f 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_13 git_last_commit: 3de669e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/casper_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/casper_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/casper_2.26.0.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: 111 Package: CATALYST Version: 1.16.2 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) Archs: i386, x64 MD5sum: 26f23531e16e1f4658fdb8050888969a 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_13 git_last_commit: 5e3b4f4 git_last_commit_date: 2021-07-13 Date/Publication: 2021-07-13 source.ver: src/contrib/CATALYST_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/CATALYST_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.1/CATALYST_1.16.2.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, treekoR dependencyCount: 242 Package: Category Version: 2.58.0 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: 72b174edd876b795bb77cd3a9e4846df 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_13 git_last_commit: 5a966e0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Category_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Category_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Category_2.58.0.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: 59 Package: categoryCompare Version: 1.36.0 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 License: GPL-2 MD5sum: 8edbc2df0fd32ca7a37092c09590a373 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_13 git_last_commit: 325b381 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/categoryCompare_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/categoryCompare_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/categoryCompare_1.36.0.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: 98 Package: CausalR Version: 1.24.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 361104a2c5c80477a48f793a05f0a9d1 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_13 git_last_commit: 4a392e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CausalR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CausalR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CausalR_1.24.0.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: 11 Package: cbaf Version: 1.14.0 Depends: R (>= 3.5.0) Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots, grDevices, stats, utils, openxlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 7aa310737722b7568f4652bd08f554a6 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, 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_13 git_last_commit: 3fcea28 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cbaf_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cbaf_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cbaf_1.14.0.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: 83 Package: cBioPortalData Version: 2.4.10 Depends: R (>= 4.0.0), AnVIL, 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: ea83d967082a408e7ca9fe1111f4486d NeedsCompilation: no Title: Exposes and makes available data from the cBioPortal web resources Description: The cBioPortalData package takes compressed resources from repositories such as cBioPortal and assembles a MultiAssayExperiment object with Bioconductor classes. 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_13 git_last_commit: e5d1319 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/cBioPortalData_2.4.10.tar.gz win.binary.ver: bin/windows/contrib/4.1/cBioPortalData_2.4.10.zip mac.binary.ver: bin/macosx/contrib/4.1/cBioPortalData_2.4.10.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html vignetteTitles: cBioPortal User Guide, cBioPortal Data Build Errors, cBioPortal Quick-start Guide 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 dependencyCount: 118 Package: cbpManager Version: 1.0.2 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: f914e83e6062b39fb5ff89f628778c47 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_13 git_last_commit: 9598ce4 git_last_commit_date: 2021-08-04 Date/Publication: 2021-08-05 source.ver: src/contrib/cbpManager_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/cbpManager_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/cbpManager_1.0.2.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: 79 Package: ccfindR Version: 1.12.0 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) MD5sum: 630e8984c10f23498c6a4d207a708678 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_13 git_last_commit: d3220ba git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ccfindR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccfindR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccfindR_1.12.0.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: 1.18.0 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 Archs: i386, x64 MD5sum: 1ab5bd227f78704f2b272b1362c04652 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_13 git_last_commit: 7f8150c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/ccmap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccmap_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccmap_1.18.0.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: 60 Package: CCPROMISE Version: 1.18.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: ae77835ff40d3572a4c76adc9e49597b 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_13 git_last_commit: 69c49fa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CCPROMISE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CCPROMISE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CCPROMISE_1.18.0.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: 53 Package: ccrepe Version: 1.28.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: 74136774f8a95c9278fef03507c324c9 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_13 git_last_commit: fd49cc5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ccrepe_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccrepe_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccrepe_1.28.0.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: 1.10.0 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: ac3e928a5ad206ecc2c2a4b97fc9e9b4 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_13 git_last_commit: 633ad23 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/celaref_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/celaref_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/celaref_1.10.0.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: 79 Package: celda Version: 1.8.1 Depends: R (>= 4.0) 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, SingleCellExperiment, dbscan, DelayedArray, stringr, Matrix, ComplexHeatmap, multipanelfigure, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, M3DExampleData, TENxPBMCData, singleCellTK License: MIT + file LICENSE MD5sum: 3f5bf2a70d361085d3d5d13c3099a2db 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], Sean Corbett [aut], Yusuke Koga [aut], Shiyi Yang [aut], Eric Reed [aut], Zhe Wang [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_13 git_last_commit: f4cf6f05 git_last_commit_date: 2021-05-27 Date/Publication: 2021-05-30 source.ver: src/contrib/celda_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/celda_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/celda_1.8.1.tgz vignettes: vignettes/celda/inst/doc/celda.pdf, vignettes/celda/inst/doc/decontX.pdf 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: 138 Package: CellaRepertorium Version: 1.2.0 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 License: GPL-3 Archs: i386, x64 MD5sum: 38e2c328a5909121a1051692686d19cd 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], 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_13 git_last_commit: 86b7f10 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellaRepertorium_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellaRepertorium_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellaRepertorium_1.2.0.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: cellbaseR Version: 1.16.0 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) Archs: i386, x64 MD5sum: 4fef813c71599e936d0f8d532b85daf6 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_13 git_last_commit: 3bb8377 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellbaseR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellbaseR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellbaseR_1.16.0.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: 64 Package: CellBench Version: 1.8.0 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: 1e84294773bede7b35af0d6279ba0fe7 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_13 git_last_commit: aa35afe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellBench_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellBench_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellBench_1.8.0.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: 2.56.0 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: a6b27848f0d4b869b9fb9b4e02d86762 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_13 git_last_commit: 72c1d14 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellHTS2_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellHTS2_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellHTS2_2.56.0.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: 91 Package: CelliD Version: 1.0.0 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 MD5sum: 2e85faa191b38954cbe24470643780e2 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_13 git_last_commit: 4ab073e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CelliD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CelliD_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CelliD_1.0.0.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: 180 Package: cellity Version: 1.20.0 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) Archs: i386, x64 MD5sum: 188cdc4549d56ebb0f31aeb887f1a002 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_13 git_last_commit: a34d727 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellity_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellity_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellity_1.20.0.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: 86 Package: CellMapper Version: 1.18.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 2841613fc2967ee425434f5aa551b7d7 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_13 git_last_commit: 7c1a001 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellMapper_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellMapper_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellMapper_1.18.0.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: 8 Package: cellmigRation Version: 1.0.0 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: c5628538bcb14aff35c7ef5a674b28bb 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_13 git_last_commit: 5029a2f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellmigRation_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellmigRation_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellmigRation_1.0.0.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: 143 Package: CellMixS Version: 1.8.0 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: 1506c04bbef699a99731f7423eaf68d6 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_13 git_last_commit: bed2b5c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellMixS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellMixS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellMixS_1.8.0.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: 93 Package: CellNOptR Version: 1.38.0 Depends: R (>= 3.5.0), RBGL, graph, methods, hash, RCurl, Rgraphviz, XML, ggplot2 Imports: igraph, stringi, stringr, Suggests: data.table, dplyr, tidyr, readr, RUnit, BiocGenerics, Enhances: doParallel License: GPL-3 MD5sum: db5fbcc68292fc2b843a49e944eb1b3d 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: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve, E.Gjerga, A.Gabor Maintainer: A.Gabor SystemRequirements: Graphviz version >= 2.2 git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_13 git_last_commit: 8e211e0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellNOptR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellNOptR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellNOptR_1.38.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using 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: 53 Package: cellscape Version: 1.16.0 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 Archs: i386, x64 MD5sum: d45f1df5b32d00d087fae33d0701a723 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_13 git_last_commit: 33e4726 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellscape_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellscape_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellscape_1.16.0.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: 36 Package: CellScore Version: 1.12.0 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: 0c14a55539626416aa51e3ef1b0d6f3b 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_13 git_last_commit: 10431e2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellScore_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellScore_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellScore_1.12.0.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: 17 Package: CellTrails Version: 1.10.0 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: 215ba17d0a2e56c876459aaf5829049d 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_13 git_last_commit: 2aa606f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CellTrails_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellTrails_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellTrails_1.10.0.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: 77 Package: cellTree Version: 1.22.0 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: ed5ee3d7719c42b05416c74bb62b6141 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_13 git_last_commit: 65701cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cellTree_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellTree_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellTree_1.22.0.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: CEMiTool Version: 1.16.0 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: cde0a4dab48f5abc51804a9dc1171e68 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_13 git_last_commit: 6cf15bd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CEMiTool_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CEMiTool_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CEMiTool_1.16.0.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: 184 Package: censcyt Version: 1.0.0 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: 6b30a82fe6da4ea4689fd31ce2bfe7ed 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_13 git_last_commit: 916b30f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/censcyt_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/censcyt_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/censcyt_1.0.0.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: 230 Package: ceRNAnetsim Version: 1.4.0 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: ff59fd5d0e0749d6fc9320504b4bd176 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_13 git_last_commit: fd4f5da git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ceRNAnetsim_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ceRNAnetsim_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ceRNAnetsim_1.4.0.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: 1.4.5 Depends: R (>= 4.0), methods Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, methods, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils, WebGestaltR LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 MD5sum: b3351fe2f84e0ccfdea248850c1673f3 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_13 git_last_commit: bd93884 git_last_commit_date: 2021-09-09 Date/Publication: 2021-09-12 source.ver: src/contrib/CeTF_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/CeTF_1.4.5.zip mac.binary.ver: bin/macosx/contrib/4.1/CeTF_1.4.5.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: 234 Package: CFAssay Version: 1.26.0 Depends: R (>= 2.10.0) License: LGPL Archs: i386, x64 MD5sum: 1807baffb9c928f60c42d9ce917a92b8 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_13 git_last_commit: 0683af9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CFAssay_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CFAssay_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CFAssay_1.26.0.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: CGEN Version: 3.28.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE MD5sum: 15e6c64f5d0e9662c977e51f57cc9d8a 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, cre] Maintainer: Nilotpal Sanyal git_url: https://git.bioconductor.org/packages/CGEN git_branch: RELEASE_3_13 git_last_commit: ded8343 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CGEN_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGEN_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGEN_3.28.0.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: 1.52.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL Archs: i386, x64 MD5sum: 0306ece704bce0faf72d89b0bf486a85 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_13 git_last_commit: 25d9f08 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CGHbase_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHbase_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHbase_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq, ragt2ridges dependencyCount: 10 Package: CGHcall Version: 2.54.0 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: b555ed0abe09720ef05f2f0be60ec9f6 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_13 git_last_commit: b30726c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CGHcall_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHcall_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHcall_2.54.0.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: 15 Package: cghMCR Version: 1.50.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 2e76e3884f719c5077a174e7e3770c09 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_13 git_last_commit: ee62229 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cghMCR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cghMCR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cghMCR_1.50.0.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: 58 Package: CGHnormaliter Version: 1.46.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 4576d2e4c548bb2576ad6dc8a5e1bc57 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_13 git_last_commit: d0de582 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CGHnormaliter_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHnormaliter_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHnormaliter_1.46.0.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: 16 Package: CGHregions Version: 1.50.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 42170cc24f13419623da18b1d33da778 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_13 git_last_commit: 123fe62 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CGHregions_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHregions_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHregions_1.50.0.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: 11 Package: ChAMP Version: 2.22.0 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: b1beeb45f508b90ced3d60c11f6d900a 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_13 git_last_commit: 2350b07 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChAMP_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChAMP_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChAMP_2.22.0.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: 253 Package: ChemmineOB Version: 1.30.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown Enhances: ChemmineR (>= 2.13.0) License: file LICENSE MD5sum: ea2abea0c081f05f9ebecfdd0bb58b6a 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_13 git_last_commit: 792d07a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChemmineOB_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChemmineOB_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChemmineOB_1.30.0.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: 9 Package: ChemmineR Version: 3.44.0 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 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: i386, x64 MD5sum: ba81826e7515f382c4c4bc72c5925130 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_13 git_last_commit: 6a834ab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChemmineR_3.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChemmineR_3.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChemmineR_3.44.0.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, customCMPdb, eiR, fmcsR, MetID, Rcpi, BioMedR, MetaDBparse, uCAREChemSuiteCLI suggestsMe: ChemmineOB, xnet dependencyCount: 61 Package: CHETAH Version: 1.8.0 Depends: R (>= 3.6), ggplot2, SingleCellExperiment Imports: gplots, shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE Archs: i386, x64 MD5sum: 9e65b54e247ca639bc223b1b03901624 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 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_13 git_last_commit: 1eacdb8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CHETAH_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CHETAH_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CHETAH_1.8.0.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: 110 Package: ChIC Version: 1.12.0 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: 92265bc933fea1af0f407cb373a5c4ea 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_13 git_last_commit: 11f21c4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIC_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ChIC_1.12.0.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: 110 Package: Chicago Version: 1.20.0 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: 28fff879ee82bf7e9f014e07bde6264b 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_13 git_last_commit: 116655f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Chicago_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Chicago_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Chicago_1.20.0.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: 70 Package: chimeraviz Version: 1.18.0 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: d8b1b19903fc4af4b3b286fba7970b06 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_13 git_last_commit: bc2b277 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chimeraviz_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chimeraviz_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chimeraviz_1.18.0.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: 160 Package: ChIPanalyser Version: 1.14.0 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: 66cfb57702c44322091eb950403d8e83 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_13 git_last_commit: 68caeca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPanalyser_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPanalyser_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPanalyser_1.14.0.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: 53 Package: ChIPComp Version: 1.22.0 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 MD5sum: 3da2d99cb5120a8b65a4c0e301f016d0 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_13 git_last_commit: 2b6dc05 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPComp_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPComp_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPComp_1.22.0.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: 48 Package: chipenrich Version: 2.16.0 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 Archs: i386, x64 MD5sum: c2740e93fde0d24058e0bb6f47d9286e 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_13 git_last_commit: 4653bc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chipenrich_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chipenrich_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chipenrich_2.16.0.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: 1.16.0 Depends: R (>= 3.4.0), 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: 1ee31cef8492b8cdfc87b8b2d0fcc946 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_13 git_last_commit: ed02199 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPexoQual_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPexoQual_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPexoQual_1.16.0.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: 148 Package: ChIPpeakAnno Version: 3.26.4 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: b4dfeb99e9849b9b047becfc72135c02 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_13 git_last_commit: 5104b7d git_last_commit_date: 2021-09-09 Date/Publication: 2021-09-12 source.ver: src/contrib/ChIPpeakAnno_3.26.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPpeakAnno_3.26.4.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPpeakAnno_3.26.4.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: 122 Package: ChIPQC Version: 1.28.0 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) 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, BiocParallel, 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: 902a4c4eea30b12d4147462ae0efc02c 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_13 git_last_commit: 047a9a4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPQC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPQC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPQC_1.28.0.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: 195 Package: ChIPseeker Version: 1.28.3 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, 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: 18ff9e8b1a10956aac8584306dbe8cf2 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] (), 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_13 git_last_commit: 2c3e718 git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-21 source.ver: src/contrib/ChIPseeker_1.28.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPseeker_1.28.3.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPseeker_1.28.3.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: ALPS, esATAC, TCGAWorkflow, cinaR suggestsMe: curatedAdipoChIP dependencyCount: 156 Package: chipseq Version: 1.42.0 Depends: R (>= 2.10), 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 MD5sum: d43c8958c7a40bd29f4b240dac4b9484 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_13 git_last_commit: 735c9dd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chipseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chipseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chipseq_1.42.0.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 suggestsMe: GenoGAM dependencyCount: 44 Package: ChIPseqR Version: 1.46.0 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: i386, x64 MD5sum: 82429a53bd78744bc963f8b44068da79 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_13 git_last_commit: d242683 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPseqR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPseqR_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPseqR_1.46.0.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: 53 Package: ChIPsim Version: 1.46.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) Archs: i386, x64 MD5sum: 397e67464730cb61853794f52aa5645a 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_13 git_last_commit: 410a1f4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPsim_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPsim_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPsim_1.46.0.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: 44 Package: ChIPXpress Version: 1.36.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 9807da3d4fc190a70c26b750991c3144 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_13 git_last_commit: 0bc3ec6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChIPXpress_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ChIPXpress_1.36.0.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: 95 Package: chopsticks Version: 1.58.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 MD5sum: e87fe726ffe3e0a240dab8600097849e 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_13 git_last_commit: 67665da git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chopsticks_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chopsticks_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chopsticks_1.58.0.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: 2.22.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: c6c80170f476b35596ad1477b771161f 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_13 git_last_commit: f67574f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chromDraw_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromDraw_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromDraw_2.22.0.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: 18 Package: ChromHeatMap Version: 1.46.0 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 Archs: i386, x64 MD5sum: e70ee846da8701f09ad80b5030b8447a 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_13 git_last_commit: 8f112f2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ChromHeatMap_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChromHeatMap_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChromHeatMap_1.46.0.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: 72 Package: chromPlot Version: 1.20.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: cb49c1199083e16f91fcc9d5ba525349 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_13 git_last_commit: f4eb7af git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chromPlot_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromPlot_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromPlot_1.20.0.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: 75 Package: ChromSCape Version: 1.2.62 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, 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, Sushi, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future License: GPL-3 MD5sum: ff2ee0ea1f13a255fcc3d9827e88cb4b 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_13 git_last_commit: 752d173 git_last_commit_date: 2021-09-14 Date/Publication: 2021-09-14 source.ver: src/contrib/ChromSCape_1.2.62.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChromSCape_1.2.62.zip mac.binary.ver: bin/macosx/contrib/4.1/ChromSCape_1.2.62.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: 214 Package: chromstaR Version: 1.18.0 Depends: R (>= 3.3), 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 MD5sum: 6fbb3dba2abcf6d4e13157eef75dc6e9 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_13 git_last_commit: 8f44dd4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chromstaR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromstaR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromstaR_1.18.0.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: 79 Package: chromswitch Version: 1.14.0 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 Archs: i386, x64 MD5sum: 36c3eaee85a16400486a338bd1bccc8b 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_13 git_last_commit: e364a04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chromswitch_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromswitch_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromswitch_1.14.0.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: 1.14.0 Depends: R (>= 3.4) 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: i386, x64 MD5sum: df45ce98b934b87ce7f2e6fd22a7e781 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_13 git_last_commit: 2dc5547 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/chromVAR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromVAR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromVAR_1.14.0.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: 151 Package: CHRONOS Version: 1.20.0 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 License: GPL-2 MD5sum: 62c92eced247aa9d363dc075e6d1c948 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_13 git_last_commit: 696cc6f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CHRONOS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CHRONOS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CHRONOS_1.20.0.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: 1.10.1 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, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE Archs: i386, x64 MD5sum: bc16816bcf46e329679bea4d937e2374 NeedsCompilation: no Title: Precict 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_13 git_last_commit: 1d348d0 git_last_commit_date: 2021-08-26 Date/Publication: 2021-08-29 source.ver: src/contrib/cicero_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cicero_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cicero_1.10.1.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: 173 Package: CIMICE Version: 1.0.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, relations, maftools, assertthat, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 MD5sum: 5bc773b1f19d85101812c2cc8d614f98 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_13 git_last_commit: 4e2d4f7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CIMICE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CIMICE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CIMICE_1.0.0.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: 81 Package: CINdex Version: 1.20.0 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: 6a8f42f5e2e2ef1778315ec60ca0094c 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_13 git_last_commit: 9a99af4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CINdex_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CINdex_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CINdex_1.20.0.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: 47 Package: circRNAprofiler Version: 1.6.0 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: 76bd9a2ec44b96accfe2328e4b95fb0e 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_13 git_last_commit: 405f392 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/circRNAprofiler_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/circRNAprofiler_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/circRNAprofiler_1.6.0.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: 163 Package: cisPath Version: 1.32.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) MD5sum: 87f1e9b2109d9e71f0c203ea3d4f237d 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_13 git_last_commit: 32610c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cisPath_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cisPath_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cisPath_1.32.0.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: 1.4.0 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: c5ffb33ecdd7c3ab8ff6d295f47f37db 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_13 git_last_commit: aefd68a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CiteFuse_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CiteFuse_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CiteFuse_1.4.0.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 dependencyCount: 127 Package: ClassifyR Version: 2.12.0 Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel Imports: locfit, grid, utils, plyr 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, randomForest, robustbase, glmnet, class License: GPL-3 Archs: i386, x64 MD5sum: a0afa3d3eb016f17bff07e61a277288d 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, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_13 git_last_commit: ede130d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ClassifyR_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClassifyR_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClassifyR_2.12.0.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: 57 Package: cleanUpdTSeq Version: 1.30.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, GenomeInfoDb, IRanges, utils, stringr, stats Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: 2f5ad25f76aeed10fb5a71a796fc3d9c 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_13 git_last_commit: 45e4e8b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cleanUpdTSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cleanUpdTSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cleanUpdTSeq_1.30.0.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 importsMe: InPAS dependencyCount: 59 Package: cleaver Version: 1.30.0 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 (>= 2.1.4) License: GPL (>= 3) MD5sum: da5b1e77658d4224f1d1cef2a2d6814d 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_13 git_last_commit: e528be9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/cleaver_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cleaver_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cleaver_1.30.0.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 suggestsMe: RforProteomics dependencyCount: 19 Package: clippda Version: 1.42.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 7883fd848f54c3a8663e493392acd1b1 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_13 git_last_commit: 4c5fe1e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clippda_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clippda_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clippda_1.42.0.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: 34 Package: clipper Version: 1.32.0 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: 9e2c49f1a05dc4ff84a6e89906e35555 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_13 git_last_commit: 2faaf4e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clipper_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clipper_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clipper_1.32.0.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 dependencyCount: 110 Package: cliqueMS Version: 1.6.0 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) MD5sum: 29a33ea4e0765b7f3fc2a5d1fa5160f0 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_13 git_last_commit: d5ddd2d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cliqueMS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cliqueMS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cliqueMS_1.6.0.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: 100 Package: Clomial Version: 1.28.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 86f6c393a86564d49b946bfab0e53f22 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_13 git_last_commit: 40f27cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Clomial_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Clomial_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Clomial_1.28.0.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: 1.40.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 Archs: i386, x64 MD5sum: 65bd99eb085c3a9a3e824a3907fbe654 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_13 git_last_commit: a298581 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Clonality_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Clonality_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Clonality_1.40.0.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: 1.30.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 31660de5f5d40bec13a644d3f179df2f 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/ git_url: https://git.bioconductor.org/packages/clonotypeR git_branch: RELEASE_3_13 git_last_commit: c404ad8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clonotypeR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clonotypeR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clonotypeR_1.30.0.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: 1.40.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: f62e88cfc56a9e8766949cb411929299 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_13 git_last_commit: 1ec5f0e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clst_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clst_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clst_1.40.0.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: 1.40.1 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: f060aa364fcd19f8b9cbbdba37a7d6b1 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_13 git_last_commit: 97804e0 git_last_commit_date: 2021-10-07 Date/Publication: 2021-10-10 source.ver: src/contrib/clstutils_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/clstutils_1.40.1.zip mac.binary.ver: bin/macosx/contrib/4.1/clstutils_1.40.1.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: 1.8.0 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: f007dc9464cc0024facf72a83255fdb2 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_13 git_last_commit: d3ba495 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CluMSID_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CluMSID_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CluMSID_1.8.0.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: 1.20.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: aaeed7cb033ee7d2495ceba024b3e726 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_13 git_last_commit: 9d5ce5d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clustComp_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clustComp_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clustComp_1.20.0.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: 2.12.0 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 License: Artistic-2.0 MD5sum: 3e92c9260ea9bddfe5c3fbc55b77e713 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_13 git_last_commit: 3dc45a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clusterExperiment_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterExperiment_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterExperiment_2.11.3.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: slingshot, tradeSeq dependencyCount: 151 Package: ClusterJudge Version: 1.14.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 6a6549fde560a2744724a2ca32e25b3e 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_13 git_last_commit: 450a0c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ClusterJudge_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClusterJudge_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClusterJudge_1.14.0.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: 21 Package: clusterProfiler Version: 4.0.5 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 Archs: i386, x64 MD5sum: 315e7070d6a5ec7976e076e90afe1b05 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], Meijun Chen [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_13 git_last_commit: 1b76287 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/clusterProfiler_4.0.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterProfiler_4.0.5.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterProfiler_4.0.5.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, DAPAR, debrowser, eegc, enrichTF, esATAC, famat, fcoex, GDCRNATools, IRISFGM, MAGeCKFlute, methylGSA, miRspongeR, MoonlightR, multiSight, netboxr, PFP, RNASeqR, signatureSearch, TCGAbiolinksGUI, TimiRGeN, ExpHunterSuite, recountWorkflow, TCGAWorkflow, genekitr, immcp, pathwayTMB, RVA, SEAA, tinyarray suggestsMe: ChIPseeker, cola, DOSE, enrichplot, epihet, GeneTonic, GenomicSuperSignature, GOSemSim, GSEAmining, MesKit, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, org.Mxanthus.db, cRegulome, GeoTcgaData dependencyCount: 125 Package: clusterSeq Version: 1.16.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 21b6c5f665615fa5b03ee774660eba2c 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_13 git_last_commit: 1b3a71b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clusterSeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterSeq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterSeq_1.16.0.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: 33 Package: ClusterSignificance Version: 1.20.0 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: 944841eefb2b4f63a531d18c168870f8 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_13 git_last_commit: 493db1d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ClusterSignificance_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClusterSignificance_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClusterSignificance_1.20.0.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: 1.64.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 4e76b49f6b61ac18bdad2d19d8242679 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_13 git_last_commit: cb54c0e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clusterStab_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterStab_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterStab_1.64.0.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: 7 Package: clustifyr Version: 1.4.0 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: 3998f3ce3b920f9e64cf35f84406f0d6 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, cre], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb] Maintainer: Rui Fu 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_13 git_last_commit: 7a53859 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/clustifyr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clustifyr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clustifyr_1.4.0.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: 96 Package: CMA Version: 1.50.0 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: 7621773a3ef69d483f1ac169eff9fae0 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_13 git_last_commit: 2f87021 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CMA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CMA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CMA_1.50.0.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: 7 Package: cmapR Version: 1.4.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle License: file LICENSE MD5sum: 2c27b7a0d154fe0fcb56c35e106f3d5e 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_13 git_last_commit: e2fd1ea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cmapR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cmapR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cmapR_1.4.0.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: 37 Package: cn.farms Version: 1.40.0 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) MD5sum: 6748fba8f92f20900a1b4f12bea6127a 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_13 git_last_commit: 8b80539 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cn.farms_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cn.farms_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cn.farms_1.40.0.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: 56 Package: cn.mops Version: 1.38.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) MD5sum: 12879c5c449d30a1370867065e0947a3 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 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_13 git_last_commit: c1ccf44 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cn.mops_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cn.mops_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cn.mops_1.38.0.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: 31 Package: CNAnorm Version: 1.38.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: d9b914b60e166e6f988e54c6ab843a1c 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_13 git_last_commit: fd0b875 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNAnorm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNAnorm_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNAnorm_1.38.0.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: 1.28.0 Depends: R (>= 3.4) 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: i386, x64 MD5sum: c138e5e209aef008460b8b6b2a455c15 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_13 git_last_commit: fdeac98 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNEr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNEr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNEr_1.28.0.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: 1.34.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 01cfa176cabcf082c63023ce366a31d3 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_13 git_last_commit: 2fc1fa3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNORdt_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORdt_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORdt_1.34.0.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: 55 Package: CNORfeeder Version: 1.32.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 Archs: i386, x64 MD5sum: e8cb201a9fb64051e683e88f4d0b5d51 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: F.Eduati, E. Gjerga Maintainer: E.Gjerga git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_13 git_last_commit: 297743c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNORfeeder_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORfeeder_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORfeeder_1.32.0.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: 54 Package: CNORfuzzy Version: 1.34.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: b2ea09d6f691a1b47b0808ddef474464 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_13 git_last_commit: 0e3dc04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNORfuzzy_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORfuzzy_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORfuzzy_1.34.0.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: 55 Package: CNORode Version: 1.34.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 MD5sum: c366e5189e46355caaf90e48e620ea05 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Enio Gjerga git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_13 git_last_commit: 06fde26 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNORode_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORode_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORode_1.34.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 55 Package: CNTools Version: 1.48.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 7c020939e4c5dbf57f54feb7f67a6209 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_13 git_last_commit: f850d02 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNTools_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNTools_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNTools_1.48.0.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: 55 Package: CNVfilteR Version: 1.6.2 Depends: R (>= 4.0) 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 Archs: i386, x64 MD5sum: 8ed8c7a945ccf8e35a71cb9c2d404786 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_13 git_last_commit: a422dc0 git_last_commit_date: 2021-09-08 Date/Publication: 2021-09-09 source.ver: src/contrib/CNVfilteR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVfilteR_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVfilteR_1.6.2.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: 152 Package: CNVgears Version: 1.0.0 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 Archs: i386, x64 MD5sum: 7ccde09242953bcf91546bf32263d9c3 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_13 git_last_commit: 3167245 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNVgears_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVgears_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVgears_1.0.0.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: 39 Package: cnvGSA Version: 1.36.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 85334af07a287e97bd48a3d18494e17a 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_13 git_last_commit: acbd086 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cnvGSA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cnvGSA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cnvGSA_1.36.0.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: 1.0.0 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: 13e2007228b12d0646b22afce8799e9e 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_13 git_last_commit: a206cc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNViz_1.0.0.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: 168 Package: CNVPanelizer Version: 1.24.0 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: 136fe903cd18e037d6737c5c2a7bc493 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_13 git_last_commit: b0dc959 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNVPanelizer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVPanelizer_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVPanelizer_1.24.0.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: 1.8.0 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: 9c95258b63b02d7fff162cdd96bcfacb 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_13 git_last_commit: dfb9864 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNVRanger_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVRanger_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVRanger_1.8.0.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: 55 Package: CNVrd2 Version: 1.30.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 4a235a36024bf75e372ae1600a172830 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_13 git_last_commit: 0a3ceac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CNVrd2_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVrd2_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVrd2_1.30.0.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: 116 Package: CoCiteStats Version: 1.64.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 5b18f3875fc6f01faf5c9949232af7de 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_13 git_last_commit: 9a21bd3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CoCiteStats_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoCiteStats_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoCiteStats_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 47 Package: COCOA Version: 2.6.0 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: d59cff4960d995207c860512cf89d6c3 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_13 git_last_commit: 81a954d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/COCOA_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COCOA_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COCOA_2.6.0.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: 114 Package: codelink Version: 1.60.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 65431df61323f9e1355fb13fc7e0848b 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_13 git_last_commit: 73c3522 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/codelink_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/codelink_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/codelink_1.60.0.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: 50 Package: CODEX Version: 1.24.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: b7b6fc3bf1b000d743eac4f3ea671cb8 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_13 git_last_commit: 0051ee2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CODEX_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CODEX_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CODEX_1.24.0.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: 46 Package: coexnet Version: 1.14.0 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 MD5sum: b5996a22fabd204c3b63e5dcb01d2c60 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/coexnet git_branch: RELEASE_3_13 git_last_commit: bf5b2c4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/coexnet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coexnet_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coexnet_1.14.0.tgz vignettes: vignettes/coexnet/inst/doc/coexnet.pdf vignetteTitles: The title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coexnet/inst/doc/coexnet.R dependencyCount: 128 Package: CoGAPS Version: 3.12.0 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 MD5sum: 03776f9fbc37f0a92fed9d3451927042 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 , Melanie L. Loth VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_13 git_last_commit: c297d7e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CoGAPS_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoGAPS_3.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoGAPS_3.12.0.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: 44 Package: cogena Version: 1.26.0 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: c3522a366794c95f89d3cff5d6f3b07f 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_13 git_last_commit: 62d798c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cogena_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cogena_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cogena_1.26.0.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: 128 Package: coGPS Version: 1.36.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: d898d1c27e10ef91524f436d2f825de8 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_13 git_last_commit: cba5151 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/coGPS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coGPS_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coGPS_1.36.0.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: 1.38.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 MD5sum: e6db005c50c99797c85756b25711ef24 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_13 git_last_commit: 706e19c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/COHCAP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COHCAP_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COHCAP_1.38.0.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: 1.8.1 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 License: MIT + file LICENSE MD5sum: f52c7fd88e3475eea17199908b34e589 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_13 git_last_commit: ffd39a8 git_last_commit_date: 2021-07-15 Date/Publication: 2021-07-18 source.ver: src/contrib/cola_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cola_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cola_1.8.1.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 65 Package: combi Version: 1.4.0 Depends: R (>= 3.5.0) 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 Archs: i386, x64 MD5sum: f60c30036e3a7f9bced73fdd1cdd943f NeedsCompilation: no Title: Compositional omics model based visual integration Description: Combine quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration is available, the results are shown as interpretable multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_13 git_last_commit: c7a1fa5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/combi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/combi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/combi_1.4.0.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: 95 Package: coMET Version: 1.24.0 Depends: R (>= 3.7.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: c01e3fcb9feeaf846b89adb2e582df4e 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_13 git_last_commit: fb8047d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/coMET_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coMET_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coMET_1.24.0.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: 148 Package: compartmap Version: 1.10.0 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: 00a55eb2feabce29511d21d49cb5f27f 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_13 git_last_commit: 4c305de git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/compartmap_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/compartmap_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/compartmap_1.10.0.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: 1.30.0 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 MD5sum: 0810fd8857b75e7dffcdfa1c8a7cd5a0 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_13 git_last_commit: 00c36c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/COMPASS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COMPASS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COMPASS_1.30.0.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: 65 Package: compcodeR Version: 1.28.0 Depends: sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, utils, stats, grDevices, graphics Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, testthat Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 619bd78f2a05dbef48e1c25e4f500872 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] () 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_13 git_last_commit: 16b062e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/compcodeR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/compcodeR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/compcodeR_1.28.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R dependencyCount: 75 Package: compEpiTools Version: 1.26.0 Depends: R (>= 3.1.1), 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 Archs: i386, x64 MD5sum: 9a7b0f246a06641578a53264bd2ba84b 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_13 git_last_commit: cefdb04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/compEpiTools_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/compEpiTools_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/compEpiTools_1.26.0.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: 153 Package: CompGO Version: 1.28.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: e4179b0a1093bf2c10942c757e5b104b NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/CompGO git_branch: RELEASE_3_13 git_last_commit: e8e77da git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CompGO_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CompGO_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CompGO_1.28.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R dependencyCount: 120 Package: ComplexHeatmap Version: 2.8.0 Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, Cairo, digest, IRanges, matrixStats, foreach, doParallel 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 License: MIT + file LICENSE MD5sum: 0bee5e12ddbda1d89536cc63238c13c9 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_13 git_last_commit: 1bd0c3b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ComplexHeatmap_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ComplexHeatmap_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ComplexHeatmap_2.8.0.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, recoup, countToFPKM importsMe: airpart, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module, CATALYST, celda, CeTF, COCOA, cola, DEComplexDisease, DEGreport, DEP, diffcyt, diffUTR, ELMER, fCCAC, GeneTonic, GenomicSuperSignature, gmoviz, InteractiveComplexHeatmap, InterCellar, iSEE, LineagePulse, MatrixQCvis, MesKit, MOMA, muscat, musicatk, MWASTools, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, sechm, SEtools, simplifyEnrichment, singleCellTK, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow, armada, conos, MKomics, pkgndep, rKOMICS, RVA, sigQC, tidyHeatmap, wilson suggestsMe: ALPS, artMS, bambu, BrainSABER, clustifyr, CNVRanger, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute, projectR, scDblFinder, TCGAbiolinks, TCGAutils, TimeSeriesExperiment, weitrix, NanoporeRNASeq, circlize, eclust, i2dash, IOHanalyzer, MOSS, multipanelfigure, spiralize dependencyCount: 29 Package: ComPrAn Version: 1.0.0 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 License: MIT + file LICENSE MD5sum: eca97ab303d89863edf18e32331c4af4 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_13 git_last_commit: 838acf2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ComPrAn_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ComPrAn_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ComPrAn_1.0.0.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: 99 Package: conclus Version: 1.0.0 Depends: R (>= 4.1) Imports: 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 Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, matrixStats, org.Hs.eg.db, org.Mm.eg.db, dynamicTreeCut, testthat License: GPL-3 MD5sum: c8dca1898d9b893cdf324b4eb94caa0d 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: RELEASE_3_13 git_last_commit: 1336595 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/conclus_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/conclus_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/conclus_1.0.0.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: 243 Package: condiments Version: 1.0.0 Depends: R (>= 4.1) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN License: MIT + file LICENSE MD5sum: 2d88881dfec5c93b4917e025e825a1dd 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_13 git_last_commit: f66736c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/condiments_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/condiments_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/condiments_1.0.0.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: 126 Package: CONFESS Version: 1.20.0 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: 5423a7f0339ee95fb91344e3ecf17223 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_13 git_last_commit: 5f6120a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CONFESS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CONFESS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CONFESS_1.20.0.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: 152 Package: consensus Version: 1.10.0 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: 4493b38da76218f75dbc2291e966ccc9 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_13 git_last_commit: 9601d93 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/consensus_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensus_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensus_1.10.0.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: 1.56.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 039e4a35086ad8b170c512c08c38653e 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_13 git_last_commit: 82a7165 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ConsensusClusterPlus_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ConsensusClusterPlus_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ConsensusClusterPlus_1.56.0.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, iSubGen, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 10 Package: consensusDE Version: 1.10.0 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: a0fcbb2e9066a1052381e48aac3a3fa4 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_13 git_last_commit: 1b434d7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/consensusDE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusDE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusDE_1.10.0.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: 145 Package: consensusOV Version: 1.14.0 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: d109afc26430e3203d1753f8c67255a7 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_13 git_last_commit: 1ff128e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/consensusOV_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusOV_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusOV_1.14.0.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: 1.20.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: c5c98bd5d561f748be6213556b5f5516 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_13 git_last_commit: e7f757a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/consensusSeekeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusSeekeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusSeekeR_1.20.0.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: 48 Package: CONSTANd Version: 1.0.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: 71921e25b4fa3cfef5f2821910c75408 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_13 git_last_commit: 3bac955 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CONSTANd_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CONSTANd_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CONSTANd_1.0.0.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: 1.20.0 Depends: 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 MD5sum: f844041b4d77a99ca16bc8e4c41ddbf9 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_13 git_last_commit: 42094e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/contiBAIT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/contiBAIT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/contiBAIT_1.20.0.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: 1.26.0 Depends: R (>= 3.0), 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: 5a015a04e5edc62a32b98239223b5432 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_13 git_last_commit: f49dd58 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/conumee_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/conumee_1.26.0.tgz 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: 144 Package: convert Version: 1.68.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 938e70253370992e1c050c9b04b079d3 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_13 git_last_commit: 4c67da1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/convert_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/convert_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/convert_1.68.0.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, maGUI dependencyCount: 10 Package: copa Version: 1.60.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 8b97df11811842e5c7d5f507c20b63fc 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_13 git_last_commit: 6eea48b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/copa_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/copa_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/copa_1.60.0.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: 7 Package: copynumber Version: 1.32.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 44339f7100d9bc942a592d40e8be6407 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_13 git_last_commit: 7a14096 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/copynumber_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/copynumber_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/copynumber_1.32.0.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: 17 Package: CopyNumberPlots Version: 1.8.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 413d710191dcc92b57a67fbb0747649f 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_13 git_last_commit: 474e67e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CopyNumberPlots_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CopyNumberPlots_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CopyNumberPlots_1.8.0.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: 150 Package: CopywriteR Version: 2.24.0 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: c60a71dea168c07050395a626ac0ad7f 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_13 git_last_commit: 5957876 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CopywriteR_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CopywriteR_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CopywriteR_2.24.0.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: 49 Package: coRdon Version: 1.10.0 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: 020e4de07d783f641eeb6c048d5ca9ef 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_13 git_last_commit: 9387b5f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/coRdon_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coRdon_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coRdon_1.10.0.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: 59 Package: CoRegNet Version: 1.30.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 MD5sum: 0904b0cecc828e18c12ba99cda344ac3 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_13 git_last_commit: 340f25d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CoRegNet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoRegNet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoRegNet_1.30.0.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: 41 Package: CoreGx Version: 1.4.2 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL-3 MD5sum: 0cfe3b00d11e2b300f821c593df70205 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_13 git_last_commit: 32dc73a git_last_commit_date: 2021-09-28 Date/Publication: 2021-09-30 source.ver: src/contrib/CoreGx_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoreGx_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/CoreGx_1.4.2.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: 116 Package: Cormotif Version: 1.38.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 Archs: i386, x64 MD5sum: ca6b1f08d8c2941a8793c9984e0007a3 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_13 git_last_commit: 48a8008 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Cormotif_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cormotif_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cormotif_1.38.0.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: 14 Package: corral Version: 1.2.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, testthat License: GPL-2 MD5sum: 4e734c0d4e1d9d314cf61b94d12d6fef 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 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, 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_13 git_last_commit: 1c730af git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/corral_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/corral_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/corral_1.2.0.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: FALSE 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: 1.58.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 6cfcebb8a2762215cf3d8c4a73c7b9b2 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_13 git_last_commit: d6cf444 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CORREP_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CORREP_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CORREP_1.58.0.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: 1.16.0 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: 74ab6897b3f500fd6f66d6db97fc9f4c 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_13 git_last_commit: e1d4441 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/coseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coseq_1.16.0.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: 1.26.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: ddfcc872924bf5b2ecdf30ef8e6e9396 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_13 git_last_commit: 4491dc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cosmiq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cosmiq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cosmiq_1.26.0.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: 96 Package: cosmosR Version: 1.0.1 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, igraph, dplyr, utils, stringr, readr, rlang, tibble, purrr, AnnotationDbi, biomaRt, org.Hs.eg.db, visNetwork Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 6c1996d8903551932896e3326d407103 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 [aut] (), Katharina Zirngibl [cre, aut] () Maintainer: Katharina Zirngibl 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_13 git_last_commit: 6d16ee7 git_last_commit_date: 2021-06-22 Date/Publication: 2021-06-24 source.ver: src/contrib/cosmosR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cosmosR_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cosmosR_1.0.1.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 110 Package: COSNet Version: 1.26.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: fb11722b842014444c92e4e350ba5fa2 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_13 git_last_commit: 7f28084 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/COSNet_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COSNet_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COSNet_1.26.0.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: CountClust Version: 1.20.0 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) MD5sum: c80e7ef12f8158eb285a9cc6570c844a 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 git_url: https://git.bioconductor.org/packages/CountClust git_branch: RELEASE_3_13 git_last_commit: 8a70b18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CountClust_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CountClust_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CountClust_1.20.0.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R dependencyCount: 60 Package: countsimQC Version: 1.10.0 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) Archs: i386, x64 MD5sum: 47dd82d9d7f425d3759ac3a38c5e7cbd 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_13 git_last_commit: a1da5e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/countsimQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/countsimQC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/countsimQC_1.10.0.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: 121 Package: covEB Version: 1.18.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: 96fd0b6a714d040595bcb9e465d5c984 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_13 git_last_commit: 7598cd6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/covEB_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/covEB_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/covEB_1.18.0.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: 17 Package: CoverageView Version: 1.30.0 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: da8b2cfc7790747d4be43156434e9953 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_13 git_last_commit: f760e2e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CoverageView_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/CoverageView_1.30.0.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: 44 Package: covRNA Version: 1.18.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 27431112d8c45e5c86fe441224253868 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_13 git_last_commit: 05e7528 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/covRNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/covRNA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/covRNA_1.18.0.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: 1.24.0 Depends: R (>= 2.10), 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: 5f5f3589c0b2ba218802a2aa9c9f6c06 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_13 git_last_commit: 7b0ad2e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cpvSNP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cpvSNP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cpvSNP_1.24.0.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: 116 Package: cqn Version: 1.38.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: e6d5389540e0ac33917e35ee65a7c658 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_13 git_last_commit: 0d15ecc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cqn_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cqn_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cqn_1.38.0.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: 19 Package: CRImage Version: 1.40.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 06b30311e1f4225949e17b0a1d4fdec2 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_13 git_last_commit: d8013b0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CRImage_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CRImage_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CRImage_1.40.0.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: 43 Package: CRISPRseek Version: 1.32.0 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, BiocParallel, hash, methods,reticulate,rhdf5 Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 730398427f664014d2dc8cb350d4b9d6 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, Benjamin R. Holmes, Hervé Pagès, 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_13 git_last_commit: b64de86 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CRISPRseek_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CRISPRseek_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CRISPRseek_1.32.0.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: 64 Package: crisprseekplus Version: 1.18.0 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: 130ed922b88db70cc057628b13235773 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_13 git_last_commit: bd34985 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/crisprseekplus_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crisprseekplus_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crisprseekplus_1.18.0.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: 158 Package: CrispRVariants Version: 1.20.0 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: a82c8d51a6ea1dfe76077d28bdf323a9 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_13 git_last_commit: 6789cce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CrispRVariants_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CrispRVariants_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CrispRVariants_1.20.0.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: 1.50.0 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: 44462f213cda05ab424fea107ed02de2 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_13 git_last_commit: c3560d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/crlmm_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crlmm_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crlmm_1.50.0.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: 1.18.0 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), DESeq2, 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), RColorBrewer (>= 1.1.2), 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), statmod (>= 1.4.34), SummarizedExperiment, tibble, XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat, tximportData License: MIT + file LICENSE MD5sum: 15b23ba1aca4e9ef3853f3213d96f13a 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 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 git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_13 git_last_commit: 8e42dd1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/crossmeta_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crossmeta_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crossmeta_1.18.0.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: 159 Package: CSAR Version: 1.44.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 6a2968c445a2b0b152c6e4f5fde8ee77 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_13 git_last_commit: 3eef45f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CSAR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSAR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CSAR_1.44.0.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: 17 Package: csaw Version: 1.26.0 Depends: 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 MD5sum: 138378af65a80e9d09c57fdc0e7fa85e 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_13 git_last_commit: d3850f3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/csaw_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/csaw_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/csaw_1.26.0.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, icetea, NADfinder, vulcan, BinQuasi suggestsMe: chipseqDB dependencyCount: 42 Package: CSSP Version: 1.30.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 5209df478a7964b218add26ab9266a30 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_13 git_last_commit: b0c8d53 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CSSP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSSP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CSSP_1.30.0.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: 1.4.1 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 3057f150c2626fc286a785d0e92e2855 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_13 git_last_commit: d067a65 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/CSSQ_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSSQ_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/CSSQ_1.4.1.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: 110 Package: ctc Version: 1.66.0 Depends: amap License: GPL-2 Archs: i386, x64 MD5sum: ddca457d1a093c896383117a877417ac 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_13 git_last_commit: 21b8569 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ctc_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctc_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctc_1.66.0.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: 2.0.0 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: 26b27bf06dbdcaa104eada5ae7a87ee0 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: RELEASE_3_13 git_last_commit: 54a0a69 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CTDquerier_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CTDquerier_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CTDquerier_2.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 76 Package: ctgGEM Version: 1.4.0 Depends: monocle, SummarizedExperiment, Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, methods, utils, sincell, TSCAN, destiny, HSMMSingleCell Suggests: BiocStyle, biomaRt, irlba, knitr, VGAM License: GPL(>=2) MD5sum: 127670ec33a2dfd070076a72bb9a1a46 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 and Carrie Minette Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ctgGEM git_branch: RELEASE_3_13 git_last_commit: 0e246e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ctgGEM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctgGEM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctgGEM_1.4.0.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: 136 Package: cTRAP Version: 1.10.1 Depends: R (>= 4.0) Imports: biomaRt, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, httr, limma, methods, parallel, pbapply, R.utils, readxl, reshape2, rhdf5, scales, shiny, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling License: MIT + file LICENSE MD5sum: b16a1df4c30086abe254597a116b5e19 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_13 git_last_commit: 089d9ff git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/cTRAP_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cTRAP_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cTRAP_1.10.1.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: 148 Package: ctsGE Version: 1.18.0 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: 0acb477022c094530dd91ae9dc6f0096 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_13 git_last_commit: ad3b72d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ctsGE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctsGE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctsGE_1.18.0.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: 71 Package: cummeRbund Version: 2.34.0 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: 552bb5f8cfdc6a8e97e2faf702f28385 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_13 git_last_commit: e2ae106 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cummeRbund_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cummeRbund_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cummeRbund_2.34.0.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 importsMe: meshr dependencyCount: 145 Package: customCMPdb Version: 1.2.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: fa73484d13805c13ae7fa9b749f87020 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_13 git_last_commit: 3357c4d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/customCMPdb_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/customCMPdb_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/customCMPdb_1.2.0.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: 1.32.0 Depends: R (>= 3.0.1), 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: 7819a4ec151dc2891cebd1a52a8ae6a4 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_13 git_last_commit: 6c740d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/customProDB_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/customProDB_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/customProDB_1.32.0.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: 100 Package: cyanoFilter Version: 1.0.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, ggplot2, GGally, graphics, grDevices, methods, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: a8bccabda62c56737171fba85bf1532a 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_13 git_last_commit: 3160dc7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cyanoFilter_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cyanoFilter_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cyanoFilter_1.0.0.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: 154 Package: cycle Version: 1.46.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 59f988e8ff012a2988230f22ede46776 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_13 git_last_commit: 50faf16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cycle_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cycle_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cycle_1.46.0.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: 18 Package: cydar Version: 1.16.0 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: f5583939f42a01ae34449bde0e553f76 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_13 git_last_commit: 38926f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cydar_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cydar_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cydar_1.16.0.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: 94 Package: CytoDx Version: 1.12.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr License: GPL-2 MD5sum: abf64d8662121768a0325284b4036313 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 git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_13 git_last_commit: 32996cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CytoDx_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoDx_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoDx_1.12.0.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: 50 Package: CytoGLMM Version: 1.0.0 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 Archs: i386, x64 MD5sum: 1d144c35f5eed47f40264cdede613187 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_13 git_last_commit: 6da96d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CytoGLMM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoGLMM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoGLMM_1.0.0.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 dependencyCount: 173 Package: cytolib Version: 2.4.0 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 License: file LICENSE License_restricts_use: yes MD5sum: 4d396b2150c70ec73e7ce9f0101e1433 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_13 git_last_commit: 652ea2c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/cytolib_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cytolib_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cytolib_2.4.0.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: 1.4.1 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 Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, shinytest License: GPL (>= 2) MD5sum: aa2375e4b8d82d978d06754d4d59c3cf 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_13 git_last_commit: edd9a70 git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-21 source.ver: src/contrib/cytomapper_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cytomapper_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cytomapper_1.4.1.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 dependencyCount: 110 Package: CytoML Version: 2.4.0 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.9), 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, parallel License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 21bf73aa629cae06bd45f6ae6b7eb3c6 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_13 git_last_commit: 6e64d69 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CytoML_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoML_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoML_2.4.0.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: 127 Package: CytoTree Version: 1.2.0 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: 96f3edfd49f1d4155e1b42d672c1674b 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_13 git_last_commit: a4311cc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/CytoTree_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoTree_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoTree_1.2.0.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: 241 Package: dada2 Version: 1.20.0 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: i386, x64 MD5sum: 4540dd8d746695739da07a08e3c98f7f 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_13 git_last_commit: b8a796b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dada2_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dada2_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dada2_1.20.0.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, microbial suggestsMe: mia dependencyCount: 78 Package: dagLogo Version: 1.30.0 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: i386, x64 MD5sum: 609bb8577e05a3de8de45e36164ecd2d 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_13 git_last_commit: 603fae3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dagLogo_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dagLogo_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dagLogo_1.30.0.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: 99 Package: daMA Version: 1.64.0 Imports: MASS, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 0222202e8da1ed6bf205ba6822684c6a 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_13 git_last_commit: 6a0fc3a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/daMA_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/daMA_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/daMA_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.4.0 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: ae56dd9c8405ab51eddd211d7da829ad 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_13 git_last_commit: a0da475 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DAMEfinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DAMEfinder_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DAMEfinder_1.4.0.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: 2.4.3 Depends: R (>= 3.4), 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: 0452ae1e29276486edb6439d6f9dc571 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_13 git_last_commit: 6c00662 git_last_commit_date: 2021-08-12 Date/Publication: 2021-08-12 source.ver: src/contrib/DaMiRseq_2.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/DaMiRseq_2.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/DaMiRseq_2.4.3.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: 242 Package: DAPAR Version: 1.24.8 Depends: R (>= 4.1.0) Imports: Biobase, MSnbase, tibble, RColorBrewer,stats,preprocessCore, Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, stringr, grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot, imp4p (>= 1.1), forcats, methods, DAPARdata (>= 1.22.2), siggenes, graph, lme4, readxl, highcharter, clusterProfiler, dplyr, tidyr,AnnotationDbi, tidyverse, vsn, FactoMineR, factoextra, multcomp, purrr, visNetwork, foreach, parallel, doParallel, igraph, dendextend, Mfuzz, apcluster, diptest, cluster Suggests: BiocGenerics, testthat, BiocStyle License: Artistic-2.0 MD5sum: cbb5442b2ee3f0c2de0465d87c9d64ed NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: This package contains a collection of functions for the visualisation and the statistical analysis of proteomic data. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [cre, aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb], Anais Courtier [ctb], Helene Borges [ctb], Enora Fremy [ctb] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/samWieczorek/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_13 git_last_commit: 4b52ffd git_last_commit_date: 2021-08-19 Date/Publication: 2021-08-22 source.ver: src/contrib/DAPAR_1.24.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/DAPAR_1.24.8.zip mac.binary.ver: bin/macosx/contrib/4.1/DAPAR_1.24.8.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, mi4p suggestsMe: DAPARdata dependencyCount: 294 Package: DART Version: 1.40.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: f98cb3db55121e31639563fa499e465f 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_13 git_last_commit: ebf6ae6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DART_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DART_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DART_1.40.0.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: 11 Package: dasper Version: 1.2.0 Depends: R (>= 4.0) Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, magrittr, megadepth, methods, plyranges, readr, reticulate, S4Vectors, stringr, SummarizedExperiment, tidyr Suggests: BiocStyle, covr, testthat, GenomicState, ggplot2, ggpubr, ggrepel, grid, knitcitations, knitr, recount, rmarkdown, sessioninfo, rtracklayer, tibble License: Artistic-2.0 MD5sum: 07a4332113458a5634d46752ad6c8257 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_13 git_last_commit: 9bf018b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dasper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dasper_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dasper_1.2.0.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 dependencyCount: 138 Package: dcanr Version: 1.8.0 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: 1bdda7118a9c6e896f94efc007c415ce 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_13 git_last_commit: ca50fbf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dcanr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dcanr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dcanr_1.8.0.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: 30 Package: dce Version: 1.0.0 Depends: R (>= 4.1) Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, CombinePValue, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, glue, Rgraphviz, harmonicmeanp, org.Hs.eg.db, logger Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, cowplot, dagitty, lmtest, sandwich, devtools, curatedTCGAData, TCGAutils, SummarizedExperiment License: GPL-3 MD5sum: 84a72ddae24812ca80b5c3af07d9cd2b 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_13 git_last_commit: a66589d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dce_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dce_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dce_1.0.0.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: 234 Package: dcGSA Version: 1.20.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 82aadb34484631987b7a8ac2062ba829 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_13 git_last_commit: 3c660d9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dcGSA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dcGSA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dcGSA_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: ddCt Version: 1.48.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 28790fa3bfed64f83cef15a48b386f20 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_13 git_last_commit: 7456707 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ddCt_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ddCt_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ddCt_1.48.0.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: 12 Package: ddPCRclust Version: 1.12.0 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: fc72ecedd74a5349756efe23e34a7dd0 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_13 git_last_commit: edc91db git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ddPCRclust_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ddPCRclust_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ddPCRclust_1.12.0.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: 1.4.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, ggplot2, KernSmooth, matrixStats, methods, parallel, pbapply, stats, statmod Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: b0c0828bea8f7737358dec0ca3155b3a 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: 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 Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2019). dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, *bioRxiv* 635714. 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] 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_13 git_last_commit: 738a4a7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dearseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dearseq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dearseq_1.4.0.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 dependencyCount: 44 Package: debCAM Version: 1.10.0 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: 7123accf3a3735a530f6dda097fc10c1 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_13 git_last_commit: 86afedd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/debCAM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/debCAM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/debCAM_1.10.0.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: 116 Package: debrowser Version: 1.20.1 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: 501243e26cd9048b0789201ab52897c2 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_13 git_last_commit: 3379462 git_last_commit_date: 2021-08-11 Date/Publication: 2021-08-12 source.ver: src/contrib/debrowser_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/debrowser_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/debrowser_1.20.1.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: 210 Package: DECIPHER Version: 2.20.0 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: 2fbd3feae7aee1a8e9fd2c7dae596408 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_13 git_last_commit: bcd35a8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DECIPHER_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DECIPHER_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DECIPHER_2.20.0.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 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 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 dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: mia, openPrimeR, AssessORFData, ensembleTax, microbial suggestsMe: MicrobiotaProcess, pagoo dependencyCount: 34 Package: deco Version: 1.8.0 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 License: GPL (>=3) MD5sum: 4402e842eba0843545aee4e2ef3dc591 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_13 git_last_commit: b3606bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/deco_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deco_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deco_1.8.0.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: 117 Package: DEComplexDisease Version: 1.12.0 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 Archs: i386, x64 MD5sum: 02faa33dc0bf7f8c43a0ec95d74270e4 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_13 git_last_commit: 33dd72a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEComplexDisease_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEComplexDisease_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEComplexDisease_1.12.0.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: 108 Package: decompTumor2Sig Version: 2.8.0 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 MD5sum: 903588aafee014039343824a74154dfb 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_13 git_last_commit: 974c2b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/decompTumor2Sig_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/decompTumor2Sig_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/decompTumor2Sig_2.8.0.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: 122 Package: DeconRNASeq Version: 1.34.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 Archs: i386, x64 MD5sum: ed8ad80c671c11ddbbb05d3f6ca03dba 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_13 git_last_commit: 69e542f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DeconRNASeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeconRNASeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeconRNASeq_1.34.0.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: 46 Package: decontam Version: 1.12.0 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: 32521a613d636569c1685f6234ee5f5d 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_13 git_last_commit: 20b09b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/decontam_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/decontam_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/decontam_1.12.0.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: 44 Package: decoupleR Version: 1.0.0 Depends: R (>= 4.0) Imports: broom, dplyr, GSVA, magrittr, Matrix, purrr, rlang, speedglm, stats, stringr, tibble, tidyr, tidyselect, viper, withr Suggests: BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, testthat License: GPL-3 MD5sum: 839daedb272bf0444bb11463bbe70330 NeedsCompilation: no Title: Package to decouple gene sets from statistics Description: Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Downstream analysis tools can be used to summarize deregulation events into a smaller set of biologically interpretable features. In particular, methods that estimate the activity of transcription factors (TFs) from gene expression are commonly used. It has been shown that the transcriptional targets of a TF yield a much more robust estimation of the TF activity than observing the expression of the TF itself. Consequently, for the estimation of transcription factor activities, a network of transcriptional regulation is required in combination with a statistical algorithm that summarizes the expression of the target genes into a single activity score. Over the years, many different regulatory networks and statistical algorithms have been developed, mostly in a fixed combination of one network and one algorithm. To systematically evaluate both networks and algorithms, we developed decoupleR , an R package that allows users to apply efficiently any combination provided. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Jesús Vélez [cre, aut] (), Christian H. Holland [aut] () Maintainer: Jesús Vélez 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_13 git_last_commit: 2d54fd6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/decoupleR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/decoupleR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/decoupleR_1.0.0.tgz vignettes: vignettes/decoupleR/inst/doc/decoupleR.html vignetteTitles: Introduction to decoupleR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R dependencyCount: 109 Package: DeepBlueR Version: 1.18.0 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: 587ed5c34f8eef9523cc078a9d934ef1 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_13 git_last_commit: 96a5591 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DeepBlueR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeepBlueR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeepBlueR_1.18.0.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: 80 Package: DeepPINCS Version: 1.0.2 Depends: keras, tensorflow, R (>= 4.1) Imports: CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: f90cccb077ff206d4241f219fb60d531 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_13 git_last_commit: 71f9817 git_last_commit_date: 2021-08-27 Date/Publication: 2021-08-29 source.ver: src/contrib/DeepPINCS_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeepPINCS_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/DeepPINCS_1.0.2.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 dependencyCount: 143 Package: deepSNV Version: 1.38.0 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: dcf6cfd4d1808e6913f51624d502ba01 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_13 git_last_commit: c542997 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/deepSNV_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deepSNV_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deepSNV_1.38.0.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 suggestsMe: GenomicFiles dependencyCount: 100 Package: DEFormats Version: 1.20.0 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 MD5sum: d71a0023d81a9e2419629b0822d91341 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_13 git_last_commit: ae1b30e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEFormats_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEFormats_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEFormats_1.20.0.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: 1.2.0 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) Archs: i386, x64 MD5sum: aa4d4eda6d86fcf9b6b05689891a8ca5 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_13 git_last_commit: 4194859 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DegNorm_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DegNorm_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DegNorm_1.2.0.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: 1.44.0 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: d76bef855a9f02c439417e67c48989be 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_13 git_last_commit: 49e7998 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEGraph_1.44.0.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: 75 Package: DEGreport Version: 1.28.0 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 MD5sum: 83052fb100579c58c49429a5dfbbb9ab 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_13 git_last_commit: c9d2eef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEGreport_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEGreport_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEGreport_1.28.0.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: 136 Package: DEGseq Version: 1.46.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 2531f9d60f03a21f66375e52f739ae85 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_13 git_last_commit: f56cc1c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEGseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEGseq_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEGseq_1.46.0.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: 45 Package: DelayedArray Version: 0.18.0 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 MD5sum: 51b4b685e8d0962a1cc3a66775630543 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_13 git_last_commit: 4e18a4f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DelayedArray_0.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedArray_0.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedArray_0.18.0.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 importsMe: batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr, celaref, celda, ChromSCape, clusterExperiment, compartmap, cytomapper, DEScan2, DropletUtils, DSS, ELMER, flowWorkspace, FRASER, GenoGAM, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, mbkmeans, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, mumosa, netSmooth, NewWave, PCAtools, ResidualMatrix, RTCGAToolbox, ScaledMatrix, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle, signatureSearch, SingleCellExperiment, SingleR, SummarizedExperiment, TSCAN, VariantExperiment, velociraptor, weitrix, zellkonverter, celldex, imcdatasets, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, CNVgears, gwascat, iSEE, MAST, S4Vectors, satuRn, SQLDataFrame, TrajectoryUtils, digitalDLSorteR dependencyCount: 15 Package: DelayedDataFrame Version: 1.8.0 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: i386, x64 MD5sum: 32fad8ed32a58b573d97ebd5367d6e0f 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_13 git_last_commit: df21f97 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DelayedDataFrame_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedDataFrame_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedDataFrame_1.8.0.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 dependsOnMe: VariantExperiment dependencyCount: 16 Package: DelayedMatrixStats Version: 1.14.3 Depends: MatrixGenerics (>= 1.4.3), DelayedArray (>= 0.17.6) Imports: methods, matrixStats (>= 0.60.0), sparseMatrixStats, Matrix, S4Vectors (>= 0.17.5), IRanges (>= 2.25.10) Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem, HDF5Array License: MIT + file LICENSE Archs: i386, x64 MD5sum: c6992f7cb6c853595e18e9328ec863a9 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_13 git_last_commit: b8bf535 git_last_commit_date: 2021-08-25 Date/Publication: 2021-08-26 source.ver: src/contrib/DelayedMatrixStats_1.14.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedMatrixStats_1.14.3.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedMatrixStats_1.14.3.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: batchelor, biscuiteer, bsseq, compartmap, dmrseq, DropletUtils, FRASER, glmGamPoi, GSVA, methrix, methylSig, mia, minfi, mumosa, PCAtools, scater, scMerge, scran, scuttle, singleCellTK, SingleR, weitrix, celldex suggestsMe: DelayedArray, MatrixGenerics, mbkmeans, SCArray, scPCA, TrajectoryUtils, digitalDLSorteR dependencyCount: 18 Package: DelayedRandomArray Version: 1.0.0 Depends: DelayedArray Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 MD5sum: fc90d4c433ee1a7efa0a101576edaccb 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_13 git_last_commit: 03b918c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DelayedRandomArray_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedRandomArray_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedRandomArray_1.0.0.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 dependencyCount: 20 Package: deltaCaptureC Version: 1.6.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2 Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 66138e764239603c681749485d0b7462 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_13 git_last_commit: 1bc5aff git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/deltaCaptureC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deltaCaptureC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deltaCaptureC_1.6.0.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: 93 Package: deltaGseg Version: 1.32.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 Archs: i386, x64 MD5sum: 351c37b10d87a51091b2ee07772c707a 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_13 git_last_commit: 59cd572 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/deltaGseg_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deltaGseg_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deltaGseg_1.32.0.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: 57 Package: DeMAND Version: 1.22.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: e809281ffb7e06d6d818cc84702b9e1e 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_13 git_last_commit: 3803d5d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DeMAND_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeMAND_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeMAND_1.22.0.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: 1.8.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 MD5sum: f714922ad87c7dbb85966167eb6c1e6d 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: Shaolong Cao, Peng Yang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_13 git_last_commit: bea3935 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DeMixT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeMixT_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeMixT_1.8.0.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: 69 Package: densvis Version: 1.2.0 Imports: Rcpp, basilisk, assertthat, reticulate LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat License: MIT + file LICENSE MD5sum: c235d7bc954577dc1ea12fa7148ba14f 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_13 git_last_commit: ceda85d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/densvis_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/densvis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/densvis_1.2.0.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: 23 Package: DEP Version: 1.14.0 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: i386, x64 MD5sum: 1fdb7a402180c74fd246ca7b9c5ab1a4 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_13 git_last_commit: 200efdf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEP_1.14.0.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: 1.8.0 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, reshape2, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 9724b43faa1db33fd28efb1eb86db16f 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_13 git_last_commit: 7eee7d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DepecheR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DepecheR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DepecheR_1.8.0.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: 85 Package: DEqMS Version: 1.10.0 Depends: R(>= 3.5),graphics,stats,ggplot2,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,plyr,matrixStats,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: 9b3d664a2c8c3d8223f8bd8b14ce8258 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_13 git_last_commit: fde4809 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEqMS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEqMS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEqMS_1.10.0.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: 39 Package: derfinder Version: 1.26.0 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 Archs: i386, x64 MD5sum: 8031467f2ec2ab0750d84289dfa8fa5d 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_13 git_last_commit: 7fc343a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/derfinder_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinder_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinder_1.26.0.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, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 148 Package: derfinderHelper Version: 1.26.0 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: f1256989686695066e60f1e89abaaf74 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. 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_13 git_last_commit: 3e8d7b1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/derfinderHelper_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinderHelper_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinderHelper_1.26.0.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: 13 Package: derfinderPlot Version: 1.26.0 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: 1b1bc007e7706f5e15705f83348f71f3 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. 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_13 git_last_commit: 41d3249 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/derfinderPlot_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinderPlot_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinderPlot_1.26.0.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: 165 Package: DEScan2 Version: 1.12.0 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: 8863e6b81d32ff90e84fec142d0fa0e5 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_13 git_last_commit: 004c7f5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEScan2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEScan2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEScan2_1.12.0.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: 126 Package: DESeq2 Version: 1.32.0 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) MD5sum: cf6f5468280b2282d587bec61fc9cb5c 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_13 git_last_commit: d2820e0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DESeq2_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DESeq2_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DESeq2_1.32.0.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, Brundle, DRomics importsMe: Anaquin, animalcules, APAlyzer, BioNERO, BRGenomics, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, crossmeta, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, EBSEA, eegc, ERSSA, GDCRNATools, GeneTonic, GenoGAM, Glimma, HTSFilter, icetea, ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer, MLSeq, multiSight, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat, pcaExplorer, phantasus, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, RNASeqR, scBFA, scGPS, SEtools, singleCellTK, SNPhood, spatialHeatmap, srnadiff, systemPipeR, systemPipeTools, TBSignatureProfiler, TimeSeriesExperiment, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, ExpHunterSuite, recountWorkflow, cinaR, HeritSeq, HTSSIP, intePareto, MetaLonDA, microbial, wilson suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics, BioCor, BiocSet, CAGEr, compcodeR, dearseq, derfinder, diffloop, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, miRmine, OPWeight, PCAtools, phyloseq, progeny, recount, RUVSeq, scran, subSeq, SummarizedBenchmark, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, conos, FateID, GeoTcgaData, metaRNASeq, RaceID, seqgendiff, Seurat dependencyCount: 92 Package: DEsingle Version: 1.12.0 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: i386, x64 MD5sum: 92260524abc8a71cc3bcfa088f178038 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_13 git_last_commit: 48b1689 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEsingle_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEsingle_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEsingle_1.12.0.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: 37 Package: DEsubs Version: 1.18.0 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 License: GPL-3 Archs: i386, x64 MD5sum: 65e2d2236be9011e4d19cb7fb66dcd59 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_13 git_last_commit: 9c8fa85 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEsubs_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEsubs_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEsubs_1.18.0.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: 128 Package: DEWSeq Version: 1.6.0 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: 015bc489373d0bc0335c40225e91d29d 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_13 git_last_commit: 34f4829 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEWSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEWSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEWSeq_1.6.0.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 97 Package: DExMA Version: 1.0.2 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 Archs: i386, x64 MD5sum: b592375040f6b78da0ac0ac1dade0a84 NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis without eliminating those genes that are presented in at least two datasets. 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 data sets 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_13 git_last_commit: 2f24d41 git_last_commit_date: 2021-07-26 Date/Publication: 2021-07-27 source.ver: src/contrib/DExMA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/DExMA_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/DExMA_1.0.2.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: 112 Package: DEXSeq Version: 1.38.0 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: dd2484b9e02e3de6623c359c66adae4a 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_13 git_last_commit: 62dc651 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DEXSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEXSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEXSeq_1.38.0.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, rnaseqDTU importsMe: diffUTR, IntEREst suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, pasilla dependencyCount: 113 Package: DFP Version: 1.50.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 9ba0cbf35e01b2b70e92cb49c496ac7b 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_13 git_last_commit: 2f2f1c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DFP_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DFP_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DFP_1.50.0.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: 7 Package: DIAlignR Version: 2.0.0 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: e583e7393d5404dd729d1402e55bbc8b 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 , Hannes Rost 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_13 git_last_commit: fe5d1ad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DIAlignR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DIAlignR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DIAlignR_2.0.0.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: 80 Package: DiffBind Version: 3.2.7 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 MD5sum: 77c955f5339904da9e13dd04e20f95d2 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_13 git_last_commit: bf9b04a git_last_commit_date: 2021-09-13 Date/Publication: 2021-09-14 source.ver: src/contrib/DiffBind_3.2.7.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiffBind_3.2.7.zip mac.binary.ver: bin/macosx/contrib/4.1/DiffBind_3.2.7.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: 184 Package: diffcoexp Version: 1.12.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) MD5sum: e5448303ad10e07712d5ee6f96902c2a 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_13 git_last_commit: 6ec7796 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffcoexp_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffcoexp_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffcoexp_1.12.0.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 dependencyCount: 121 Package: diffcyt Version: 1.12.0 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 Archs: i386, x64 MD5sum: c81fb484f9cc406c5981cc387d036ca7 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_13 git_last_commit: 7e8f4d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffcyt_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffcyt_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffcyt_1.12.0.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 suggestsMe: CATALYST dependencyCount: 218 Package: diffGeneAnalysis Version: 1.74.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL Archs: i386, x64 MD5sum: 41fa2a635f1ecc0868249890406397ff 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_13 git_last_commit: 1a4c3f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffGeneAnalysis_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffGeneAnalysis_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffGeneAnalysis_1.74.0.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: 1.24.1 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: 393f3167ea25e5dc11bfc45398b61616 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_13 git_last_commit: a505f6d git_last_commit_date: 2021-07-15 Date/Publication: 2021-07-18 source.ver: src/contrib/diffHic_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffHic_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.1/diffHic_1.24.1.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: 55 Package: DiffLogo Version: 2.16.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: ae9b311cd45114d4ac4476912d25f976 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_13 git_last_commit: f4c40e5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DiffLogo_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiffLogo_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DiffLogo_2.16.0.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: 1.20.0 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: ba2fad9e05f0fd334b286639ec53d41d 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 git_url: https://git.bioconductor.org/packages/diffloop git_branch: RELEASE_3_13 git_last_commit: 900a5e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffloop_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffloop_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffloop_1.20.0.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: 1.12.0 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 Archs: i386, x64 MD5sum: 2da801d2978c88088124fe423d5280df 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_13 git_last_commit: 2996ef5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffuStats_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffuStats_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffuStats_1.12.0.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: 51 Package: diffUTR Version: 1.0.0 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 License: GPL-3 MD5sum: 3bfeb267299c336d7322aea6858c825c 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_13 git_last_commit: 948e593 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diffUTR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffUTR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffUTR_1.0.0.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: 141 Package: diggit Version: 1.24.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: 3d6618b9d307b117bad69dfbca533229 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_13 git_last_commit: ad4d03a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/diggit_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diggit_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diggit_1.24.0.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: dir.expiry Version: 1.0.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 0b1ab8b211eacc2dbc3d93db4dc5592a 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_13 git_last_commit: 251926f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dir.expiry_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dir.expiry_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dir.expiry_1.0.0.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: 1.18.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: c3a3f317d770ca43d94c2466a8cc1c79 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_13 git_last_commit: 54d58a4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Director_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/Director_1.18.0.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: 1.34.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 MD5sum: 9eb271cd44ecb237637dcb734ae5ac73 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_13 git_last_commit: 75a199d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DirichletMultinomial_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DirichletMultinomial_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DirichletMultinomial_1.34.0.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: 9 Package: discordant Version: 1.16.0 Depends: R (>= 3.4) Imports: Biobase, stats, biwt, gtools, MASS, tools Suggests: BiocStyle, knitr License: GPL (>= 2) MD5sum: f8f617d7b336870f507f2940b74999d1 NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al. biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [cre,aut], Katerina Kechris [aut] Maintainer: Charlotte Siska URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_13 git_last_commit: c57e96b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/discordant_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/discordant_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/discordant_1.16.0.tgz vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf vignetteTitles: Discordant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R dependencyCount: 20 Package: DiscoRhythm Version: 1.8.0 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 MD5sum: 3fd336f36dc1674722d52659a967081d 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_13 git_last_commit: 1ea451c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DiscoRhythm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiscoRhythm_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DiscoRhythm_1.8.0.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: 1.4.1 Depends: R (>= 4.0) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, UpSetR License: GPL (>= 3) Archs: i386, x64 MD5sum: 14286487f256b7a3317fcb80e26f757e 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], Mark D. Robinson [aut]. 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_13 git_last_commit: 53f064d git_last_commit_date: 2021-08-19 Date/Publication: 2021-08-22 source.ver: src/contrib/distinct_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/distinct_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/distinct_1.4.1.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 dependencyCount: 90 Package: dittoSeq Version: 1.4.4 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: i386, x64 MD5sum: 855c626af1c6635b1ed8ab4d4542a3fc 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_13 git_last_commit: 922ef4d git_last_commit_date: 2021-10-11 Date/Publication: 2021-10-12 source.ver: src/contrib/dittoSeq_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/dittoSeq_1.4.4.zip mac.binary.ver: bin/macosx/contrib/4.1/dittoSeq_1.4.4.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: 67 Package: divergence Version: 1.8.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 06227281f8b74de46a679c2236c6f5a7 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_13 git_last_commit: e13dfb8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/divergence_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/divergence_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/divergence_1.8.0.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: 26 Package: dks Version: 1.38.0 Depends: R (>= 2.8) Imports: cubature License: GPL Archs: i386, x64 MD5sum: 6d348e8347f14d1456f9a6ca4791035c 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_13 git_last_commit: 445de0a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dks_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dks_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dks_1.38.0.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: 1.6.0 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: c037f6d7748f0327ff4ab32a4896d454 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_13 git_last_commit: 802cb16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMCFB_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMCFB_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMCFB_1.6.0.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: 92 Package: DMCHMM Version: 1.14.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr License: GPL-3 MD5sum: 1c3e4861b53cc3748358a90fbfecd4a6 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. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_13 git_last_commit: d913bcf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMCHMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMCHMM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMCHMM_1.14.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: Sending Messages With Gmailr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 55 Package: DMRcaller Version: 1.24.0 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: c66bc404c52b29769ee7445c59d1af11 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_13 git_last_commit: 828312c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMRcaller_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRcaller_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRcaller_1.24.0.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: 2.6.0 Depends: R (>= 3.6.0), minfi, SummarizedExperiment Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, missMethyl, GenomicRanges, methods, graphics, plyr, Gviz, IRanges, stats, utils, S4Vectors Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: file LICENSE MD5sum: 24f8ec5101b41377068b4734ab432945 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_13 git_last_commit: d7de4ca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMRcate_2.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/DMRcate_2.5.1.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: 220 Package: DMRforPairs Version: 1.28.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 58c0500b1f16ca91db44b393ee2c1c57 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_13 git_last_commit: 4f0c493 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMRforPairs_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRforPairs_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRforPairs_1.28.0.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: 143 Package: DMRScan Version: 1.14.0 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: e62a43759bd3f08da551a97ab2ba4045 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_13 git_last_commit: 8fa94c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DMRScan_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRScan_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRScan_1.14.0.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: 1.12.0 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: b51537a45ee027dfa422a3aad11065be 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_13 git_last_commit: e8485f3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dmrseq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dmrseq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dmrseq_1.12.0.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: 1.8.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 28245292f77807568bb3a10a7df815e0 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_13 git_last_commit: 4f760bf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DNABarcodeCompatibility_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNABarcodeCompatibility_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNABarcodeCompatibility_1.8.0.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: 1.22.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: cb64630dcc51a446dec6275194df9ac8 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_13 git_last_commit: 985d1f4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DNABarcodes_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNABarcodes_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNABarcodes_1.22.0.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: 1.66.0 License: GPL (>= 2) MD5sum: 03c2e5385a6560bb47443cce13ff2761 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_13 git_last_commit: d6ba71e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DNAcopy_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNAcopy_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNAcopy_1.66.0.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, MDTS, MEDIPS, MethCP, MinimumDistance, QDNAseq, Repitools, SCOPE, sesame, snapCGH, cghRA, jointseg, PSCBS suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, ACNE, aroma.cn, aroma.core, bcp, calmate dependencyCount: 0 Package: DNAshapeR Version: 1.20.0 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 MD5sum: 85d33ea1332b766c92cec1c8087342b7 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_13 git_last_commit: 20c156a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DNAshapeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNAshapeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNAshapeR_1.20.0.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: 59 Package: DominoEffect Version: 1.12.0 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: i386, x64 MD5sum: e1d830013d92fad3c46f2f823e590865 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_13 git_last_commit: bce8fe8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DominoEffect_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DominoEffect_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DominoEffect_1.12.0.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: 99 Package: doppelgangR Version: 1.20.0 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) Archs: i386, x64 MD5sum: a08bb41600a550aeae6fffb5ac8aa792 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_13 git_last_commit: 94fcf60 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/doppelgangR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/doppelgangR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/doppelgangR_1.20.0.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: 1.14.0 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 Archs: i386, x64 MD5sum: 32f489719b05a63dfbe151b30703cfd9 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_13 git_last_commit: 8a69504 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Doscheda_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Doscheda_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Doscheda_1.14.0.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: 157 Package: DOSE Version: 3.18.3 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: addf6e4e031d5a36b996aee655d8468f 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_13 git_last_commit: f01fef4 git_last_commit_date: 2021-09-30 Date/Publication: 2021-10-03 source.ver: src/contrib/DOSE_3.18.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/DOSE_3.18.3.zip mac.binary.ver: bin/macosx/contrib/4.1/DOSE_3.18.3.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, meshes, miRspongeR, MoonlightR, ReactomePA, RegEnrich, RNASeqR, scTensor, signatureSearch suggestsMe: cola, GOSemSim, MAGeCKFlute, rrvgo, scGPS, simplifyEnrichment, genekitr dependencyCount: 91 Package: doseR Version: 1.8.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: cbde374d37a1b25549721cdd139d7811 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_13 git_last_commit: ddab3b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/doseR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/doseR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/doseR_1.8.0.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: 41 Package: dpeak Version: 1.4.0 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: 9cd66574eca19b9967748aaf8bbc2fd6 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_13 git_last_commit: 893adb0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dpeak_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dpeak_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dpeak_1.4.0.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: 47 Package: drawProteins Version: 1.12.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: d85b30def3647793ab1b50758eabfdba 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_13 git_last_commit: f3e45e2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/drawProteins_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/drawProteins_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/drawProteins_1.12.0.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: 1.20.0 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: c2828c0104e50c651d036c34fddde6cf 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_13 git_last_commit: 27f2619 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DRIMSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DRIMSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DRIMSeq_1.20.0.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: 66 Package: DriverNet Version: 1.32.0 Depends: R (>= 2.10), methods License: GPL-3 Archs: i386, x64 MD5sum: 1b21984ba9872e6a8f1d1c9e8c52c0ae 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_13 git_last_commit: 91602de git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DriverNet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DriverNet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DriverNet_1.32.0.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: 1.12.3 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, 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: 4ddb9fbd03d3450a724a0ee6648c2d6d 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, cre], Jonathan Griffiths [ctb], Davis McCarthy [ctb] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_13 git_last_commit: 3f2d0d0 git_last_commit_date: 2021-09-18 Date/Publication: 2021-09-19 source.ver: src/contrib/DropletUtils_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/DropletUtils_1.12.3.zip mac.binary.ver: bin/macosx/contrib/4.1/DropletUtils_1.12.3.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: 51 Package: drugTargetInteractions Version: 1.0.2 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 MD5sum: 1d31b5709c6d7e23c68737a049358817 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_13 git_last_commit: dd1665a git_last_commit_date: 2021-08-26 Date/Publication: 2021-08-29 source.ver: src/contrib/drugTargetInteractions_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/drugTargetInteractions_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/drugTargetInteractions_1.0.2.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: 101 Package: DrugVsDisease Version: 2.34.0 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: 6707ad82891dc9996e52ef44550337b3 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_13 git_last_commit: 4d15037 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DrugVsDisease_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DrugVsDisease_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DrugVsDisease_2.34.0.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: 126 Package: DSS Version: 2.40.0 Depends: R (>= 3.3), methods, Biobase, BiocParallel, bsseq Imports: utils, graphics, stats, splines, DelayedArray Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: 49b18e5a7460b262c010696d1537802c 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_13 git_last_commit: 7189a27 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DSS_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DSS_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DSS_2.40.0.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: DMRcate, kissDE, metaseqR2, MethCP, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 74 Package: DTA Version: 2.38.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 0659ba815a70b57a499d82cbbd87e24e 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_13 git_last_commit: 31af82a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DTA_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DTA_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DTA_2.38.0.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: dualKS Version: 1.52.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: 5552c482522095dc916b8b7a7cd9f5dc NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang git_url: https://git.bioconductor.org/packages/dualKS git_branch: RELEASE_3_13 git_last_commit: ee599fd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dualKS_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dualKS_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dualKS_1.52.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R dependencyCount: 13 Package: Dune Version: 1.4.0 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 Archs: i386, x64 MD5sum: 8f252152a7a015db21c416edfc9a1a30 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_13 git_last_commit: b65b866 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Dune_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Dune_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Dune_1.4.0.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: 1.22.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 1fbf8e56d03ddf5e96c11b1833202cf5 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_13 git_last_commit: f2c89b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dupRadar_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/dupRadar_1.22.0.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: 9 Package: dyebias Version: 1.52.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 34abebe6cc2e361d04960942a81aaee4 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_13 git_last_commit: 90e5fe4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/dyebias_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dyebias_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dyebias_1.52.0.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: 10 Package: DynDoc Version: 1.70.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: c2185987a98dc4915298021b094d3037 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_13 git_last_commit: fcb8530 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/DynDoc_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DynDoc_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DynDoc_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easyreporting Version: 1.4.0 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: c84cf0fd5fec279839514a8139207f7b 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_13 git_last_commit: b56b250 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/easyreporting_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/easyreporting_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/easyreporting_1.4.0.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: 44 Package: easyRNASeq Version: 2.28.0 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: b0b5d6410437b124d0b4b51eaf24271e 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_13 git_last_commit: 6d18522 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/easyRNASeq_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/easyRNASeq_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/easyRNASeq_2.28.0.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: 101 Package: EBarrays Version: 2.56.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) MD5sum: d5de3251a9863a5501ac17a59c5725c9 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_13 git_last_commit: 097b73e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBarrays_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBarrays_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBarrays_2.56.0.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: 11 Package: EBcoexpress Version: 1.36.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) MD5sum: 11020d3f190e66c6e891878d11362e01 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_13 git_last_commit: 3d7103b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBcoexpress_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBcoexpress_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBcoexpress_1.36.0.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: 15 Package: EBImage Version: 4.34.0 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 MD5sum: 050d351fb103eb5b12f36266abe9d1c4 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_13 git_last_commit: b4e3a23 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBImage_4.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBImage_4.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBImage_4.34.0.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, ShinyImage importsMe: bnbc, flowCHIC, heatmaps, yamss, BioImageDbs, bioimagetools, CropDetectR, ExpImage, GoogleImage2Array, LFApp, pliman, RockFab, SAFARI, trackter suggestsMe: HilbertVis, tofsims, DmelSGI, aroma.core, ijtiff, juicr, lidR, metagear, ProFound dependencyCount: 25 Package: EBSEA Version: 1.20.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: c9213bc13f17c856620b611159817061 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_13 git_last_commit: 72eef5e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSEA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSEA_1.20.0.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: 1.32.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 19f459f3de57052ccfde1233b2160675 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_13 git_last_commit: c92c0b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSeq_1.32.0.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: 1.26.0 Depends: EBSeq License: Artistic-2.0 Archs: i386, x64 MD5sum: 0da1eaba0ddb29ecd1f87222e1ea5c90 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_13 git_last_commit: 1ee1381 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EBSeqHMM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSeqHMM_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSeqHMM_1.26.0.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: 1.64.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: f9a34386b1ca4880f92546d9a75faa07 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_13 git_last_commit: 27ed302 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ecolitk_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ecolitk_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ecolitk_1.64.0.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: 7 Package: EDASeq Version: 2.26.1 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: 42c199c4f749298ab4d6633de535a4c0 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_13 git_last_commit: 49df07d git_last_commit_date: 2021-06-18 Date/Publication: 2021-06-20 source.ver: src/contrib/EDASeq_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/EDASeq_2.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/EDASeq_2.26.1.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: 106 Package: edge Version: 2.24.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE MD5sum: 8ce0c207b741d174e0e151f41eccc0ec 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_13 git_last_commit: 29ac248 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/edge_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/edge_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/edge_2.24.0.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: 110 Package: edgeR Version: 3.34.1 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) MD5sum: b8ffb43e7c9b6221361da5683f60d289 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_13 git_last_commit: 0a0c62a git_last_commit_date: 2021-09-03 Date/Publication: 2021-09-05 source.ver: src/contrib/edgeR_3.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/edgeR_3.34.1.zip mac.binary.ver: bin/macosx/contrib/4.1/edgeR_3.34.1.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, RNASeqR, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample, OSCA.workflows, babel, BALLI, BioInsight, edgeRun, GSAgm importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, autonomics, AWFisher, baySeq, 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, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR2, MIGSA, MLSeq, moanin, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PhIPData, ppcseq, PROPER, psichomics, RCM, regsplice, Repitools, ROSeq, scCB2, scde, scone, scran, SEtools, SIMD, SingleCellSignalR, singscore, spatialHeatmap, splatter, SPsimSeq, srnadiff, STATegRa, sva, systemPipeR, TBSignatureProfiler, TCseq, TimeSeriesExperiment, tradeSeq, tweeDEseq, vidger, yarn, zinbwave, emtdata, ExpHunterSuite, recountWorkflow, SingscoreAMLMutations, BinQuasi, cinaR, DGEobj.utils, digitalDLSorteR, HTSCluster, MetaLonDA, microbial, myTAI, QuasiSeq, RVA, 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, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DiPALM, GeoTcgaData, glmmSeq, seqgendiff, SIBERG dependencyCount: 10 Package: eegc Version: 1.18.0 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 MD5sum: 51928b81b4630d44f5e54d326ae67c30 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_13 git_last_commit: d0acc6a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/eegc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eegc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eegc_1.18.0.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: 156 Package: EGAD Version: 1.20.0 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 Archs: i386, x64 MD5sum: e53f37f6414a59d7c9fa7009fc669d57 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_13 git_last_commit: 948910f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EGAD_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EGAD_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EGAD_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 65 Package: EGSEA Version: 1.20.0 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), Glimma (>= 1.4.0), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 Archs: i386, x64 MD5sum: 60a5a089cdfe6c4ae0eaac82b3c21aa7 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_13 git_last_commit: 77593a7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EGSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EGSEA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EGSEA_1.20.0.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: 180 Package: eiR Version: 1.32.0 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: 72534477ae7303314111a0168dcc472a 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_13 git_last_commit: b291e60 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/eiR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eiR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eiR_1.32.0.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: 67 Package: eisaR Version: 1.4.0 Depends: R (>= 4.0.0) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR, methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, rtracklayer License: GPL-3 MD5sum: c95be545537ec4abb6036e54297973d5 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_13 git_last_commit: 907e2ad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/eisaR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eisaR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eisaR_1.4.0.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: 30 Package: ELMER Version: 2.16.0 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 (>= 2.9.2), 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, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: 01b19571f4a76f7840705f3730691c9a 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_13 git_last_commit: 31e996d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ELMER_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ELMER_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ELMER_2.16.0.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: 213 Package: EMDomics Version: 2.22.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE Archs: x64 MD5sum: e0e1f7b37cdbe476909335ed157656a6 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_13 git_last_commit: 86b3e22 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EMDomics_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EMDomics_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EMDomics_2.22.0.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: 50 Package: EmpiricalBrownsMethod Version: 1.20.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 0966bc14a84787c8f4f5cde8ba401f32 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_13 git_last_commit: d216c2c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EmpiricalBrownsMethod_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EmpiricalBrownsMethod_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EmpiricalBrownsMethod_1.20.0.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: 1.10.0 Depends: ggplot2, ggrepel Imports: ggalt, ggrastr Suggests: RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: ecfd3f3fc1491027c38dc4d964ab8be7 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_13 git_last_commit: f229674 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EnhancedVolcano_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnhancedVolcano_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EnhancedVolcano_1.10.0.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: 82 Package: EnMCB Version: 1.4.1 Depends: R (>= 4.0) Imports: foreach, doParallel, parallel, stats, survivalROC, glmnet, rms, mboost, survivalsvm, ggplot2, IlluminaHumanMethylation450kanno.ilmn12.hg19, minfi, boot, survival, utils Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, prognosticROC License: GPL-2 MD5sum: debf194044e7f0034bcbcd681bd291e0 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. A stacked ensemble of machine learning models, which combined the cox, support vector machine and elastic-net regression model, can be constructed to predict 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_13 git_last_commit: f4ab997 git_last_commit_date: 2021-09-20 Date/Publication: 2021-09-21 source.ver: src/contrib/EnMCB_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnMCB_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/EnMCB_1.4.1.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: 197 Package: ENmix Version: 1.28.8 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils,irr, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 828ee92c302037d1d971069c58f4caff 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_13 git_last_commit: e996e24 git_last_commit_date: 2021-10-05 Date/Publication: 2021-10-07 source.ver: src/contrib/ENmix_1.28.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/ENmix_1.28.8.zip mac.binary.ver: bin/macosx/contrib/4.1/ENmix_1.28.8.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: 173 Package: EnrichedHeatmap Version: 1.22.0 Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 2.5.1), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: da9ba95b7ab1dfee9a194bc9cb2f9ad7 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 Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_13 git_last_commit: ff49b7f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EnrichedHeatmap_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnrichedHeatmap_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EnrichedHeatmap_1.22.0.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: profileplyr suggestsMe: ComplexHeatmap, InteractiveComplexHeatmap dependencyCount: 41 Package: EnrichmentBrowser Version: 2.22.2 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 MD5sum: 6d0355aea7c6a6e10a992519e3b307d0 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_13 git_last_commit: e3ba414 git_last_commit_date: 2021-07-21 Date/Publication: 2021-07-22 source.ver: src/contrib/EnrichmentBrowser_2.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnrichmentBrowser_2.22.2.zip mac.binary.ver: bin/macosx/contrib/4.1/EnrichmentBrowser_2.22.2.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: 92 Package: enrichplot Version: 1.12.3 Depends: R (>= 3.5.0) Imports: cowplot, DOSE (>= 3.16.0), ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree 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 License: Artistic-2.0 MD5sum: b27db6550582409011532c8363944f7e 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. 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_13 git_last_commit: 586391d git_last_commit_date: 2021-10-08 Date/Publication: 2021-10-10 source.ver: src/contrib/enrichplot_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/enrichplot_1.12.3.zip mac.binary.ver: bin/macosx/contrib/4.1/enrichplot_1.12.3.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, multiSight, ReactomePA, ExpHunterSuite suggestsMe: methylGSA dependencyCount: 123 Package: enrichTF Version: 1.8.0 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 Archs: i386, x64 MD5sum: 91d272b46ea357e9081efac18379c306 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_13 git_last_commit: 956c94b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/enrichTF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/enrichTF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/enrichTF_1.8.0.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: 211 Package: ensembldb Version: 2.16.4 Depends: 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: 34403077a67040567ab005e3f28cdda7 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_13 git_last_commit: 130dbcc git_last_commit_date: 2021-08-04 Date/Publication: 2021-08-05 source.ver: src/contrib/ensembldb_2.16.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/ensembldb_2.16.4.zip mac.binary.ver: bin/macosx/contrib/4.1/ensembldb_2.16.4.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: APAlyzer, biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, diffUTR, epivizrData, ggbio, Gviz, ldblock, metagene, TVTB, tximeta, GenomicDistributionsData, scRNAseq, utr.annotation suggestsMe: alpine, CNVRanger, eisaR, EpiTxDb, GenomicFeatures, multicrispr, satuRn, wiggleplotr dependencyCount: 99 Package: ensemblVEP Version: 1.34.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: 0a2162c10aa913918af3dac1f2aabc06 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 104) 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_13 git_last_commit: 71a49c5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ensemblVEP_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ensemblVEP_1.34.0.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: 98 Package: epialleleR Version: 1.0.0 Depends: R (>= 4.1) Imports: stats, methods, utils, Rsamtools, GenomicRanges, BiocGenerics, GenomeInfoDb, SummarizedExperiment, VariantAnnotation, stringi, data.table LinkingTo: Rcpp, BH Suggests: RUnit, knitr, rmarkdown License: Artistic-2.0 MD5sum: 96984d5c93ffc2a060907648e015405b 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 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 VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: RELEASE_3_13 git_last_commit: b01940d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epialleleR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epialleleR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epialleleR_1.0.0.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: 99 Package: epidecodeR Version: 1.0.2 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 69afa31c303487ac5fc70e7805824d56 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_13 git_last_commit: ca8d4d8 git_last_commit_date: 2021-06-02 Date/Publication: 2021-06-03 source.ver: src/contrib/epidecodeR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/epidecodeR_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/epidecodeR_1.0.2.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: 137 Package: EpiDISH Version: 2.8.0 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: 7e8c5a9ca756e13b7a120c733bb3ed3f 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_13 git_last_commit: e034b98 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EpiDISH_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EpiDISH_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EpiDISH_2.8.0.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, FlowSorted.Blood.EPIC dependencyCount: 22 Package: epigenomix Version: 1.32.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: a23673fa33acbc98c91a0b3de5512608 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_13 git_last_commit: dc1b2d5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epigenomix_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epigenomix_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epigenomix_1.32.0.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: 104 Package: epigraHMM Version: 1.0.8 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 Archs: i386, x64 MD5sum: 91f0c1325e73f8c35515805d41029d77 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_13 git_last_commit: 3d5f844 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/epigraHMM_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/epigraHMM_1.0.8.zip mac.binary.ver: bin/macosx/contrib/4.1/epigraHMM_1.0.8.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: 147 Package: epihet Version: 1.8.0 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 MD5sum: 2913ca46c4dca11282842f2dd3973624 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_13 git_last_commit: cffe2bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epihet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epihet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epihet_1.8.0.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: 164 Package: epiNEM Version: 1.16.0 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 MD5sum: 7472f66c0e611abf0320e5043de7aec7 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_13 git_last_commit: 2383f66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epiNEM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epiNEM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epiNEM_1.16.0.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: EpiTxDb Version: 1.4.0 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: 4c96f38e1ebe292b4c97120b59aae59b 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_13 git_last_commit: 14681e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EpiTxDb_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EpiTxDb_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EpiTxDb_1.4.0.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: 114 Package: epivizr Version: 2.22.0 Depends: R (>= 3.0), 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 License: Artistic-2.0 Archs: i386, x64 MD5sum: 57c17eedb00dd887654efb0eef7bd677 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_13 git_last_commit: f93816c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epivizr_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizr_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizr_2.22.0.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 importsMe: metavizr dependencyCount: 117 Package: epivizrChart Version: 1.14.0 Depends: R (>= 3.4.0) 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: 53d95e01e8da4131f674d9456152cb69 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_13 git_last_commit: 5b50f3c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epivizrChart_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrChart_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrChart_1.14.0.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: 111 Package: epivizrData Version: 1.20.0 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: 3a3a5be3d2e05ab495d890c0b3735857 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_13 git_last_commit: ac055c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epivizrData_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrData_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrData_1.20.0.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 dependencyCount: 108 Package: epivizrServer Version: 1.20.0 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: 18eb570424d8ff0be96f366fcea5c67b 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_13 git_last_commit: af3b113 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epivizrServer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrServer_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrServer_1.20.0.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 dependencyCount: 13 Package: epivizrStandalone Version: 1.20.0 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: 96d41d5ec7b7abd4c526bd8466bd0dc8 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_13 git_last_commit: fd34eef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/epivizrStandalone_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrStandalone_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrStandalone_1.20.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr dependencyCount: 119 Package: erccdashboard Version: 1.26.0 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: 64ac5264609ec7396505bc3ce452f63a 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_13 git_last_commit: cea65f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/erccdashboard_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/erccdashboard_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/erccdashboard_1.26.0.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: 55 Package: erma Version: 1.8.0 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: 711c8d85bd43af3dc9336d1376a8674b 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_13 git_last_commit: 6958ee9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/erma_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/erma_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/erma_1.8.0.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: 135 Package: ERSSA Version: 1.10.0 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: 4cc400c8b6d366694467373d2a04538c 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_13 git_last_commit: cdcef91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ERSSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ERSSA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ERSSA_1.10.0.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: 1.14.0 Depends: R (>= 3.5), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, 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: aa7b51fbb24d051dbf3437857f11c12d 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_13 git_last_commit: 2939dd0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/esATAC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/esATAC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/esATAC_1.14.0.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: 199 Package: escape Version: 1.2.0 Depends: R (>= 4.0) Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA, SingleCellExperiment, limma, ggridges, msigdbr, stats, BiocParallel, Matrix Suggests: Seurat, SeuratObject, knitr, rmarkdown, BiocStyle, testthat, dittoSeq (>= 1.1.2) License: Apache License 2.0 MD5sum: f8d7152ea491949ec42ea370ff7a5ac5 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_13 git_last_commit: eac1d27 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/escape_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/escape_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/escape_1.2.0.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 dependencyCount: 111 Package: esetVis Version: 1.18.0 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: 749bfb9f06a69ab7973afe959e6addd7 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_13 git_last_commit: 3e8a9aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/esetVis_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/esetVis_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/esetVis_1.18.0.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: 57 Package: eudysbiome Version: 1.22.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 0a108d616549e2a71e7e84fb43acd33c 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_13 git_last_commit: 2309739 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/eudysbiome_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eudysbiome_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eudysbiome_1.22.0.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: 34 Package: evaluomeR Version: 1.8.0 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 Archs: x64 MD5sum: 401d0c0b674a8699960fa422de9acf9e 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_13 git_last_commit: b88f5c0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/evaluomeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/evaluomeR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/evaluomeR_1.8.0.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: 115 Package: EventPointer Version: 3.0.0 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 Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: a7c141ef59c8ca9406a54cb5c35059b6 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] 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_13 git_last_commit: e4f2123 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/EventPointer_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EventPointer_3.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EventPointer_3.0.0.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: 154 Package: EWCE Version: 1.0.1 Depends: R(>= 4.1), RNOmni (>= 1.0) Imports: AnnotationHub, ewceData, ExperimentHub, ggplot2, grDevices, grid, reshape2, biomaRt, limma, stringr, cowplot, HGNChelper, ggdendro, gridExtra, Matrix, methods, parallel, future, scales, SummarizedExperiment, stats, utils Suggests: devtools, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), data.table, sctransform, readxl, SingleCellExperiment, memoise, markdown License: Artistic-2.0 MD5sum: 332d18de204a132f273b6deb7255ea2e 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] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EWCE git_branch: RELEASE_3_13 git_last_commit: 680dd7d git_last_commit_date: 2021-06-17 Date/Publication: 2021-06-20 source.ver: src/contrib/EWCE_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/EWCE_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/EWCE_1.0.1.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html vignetteTitles: Expression Weighted Celltype Enrichment with EWCE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R dependencyCount: 133 Package: ExCluster Version: 1.10.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 403c8d320794692b48b2f5b186b3e687 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_13 git_last_commit: e1c1647 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExCluster_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ExCluster_1.10.0.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: 45 Package: ExiMiR Version: 2.34.0 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: e04cd03d39fedce4a3a8525c5583b754 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_13 git_last_commit: 8830d07 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExiMiR_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExiMiR_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExiMiR_2.34.0.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: 14 Package: exomeCopy Version: 1.38.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) MD5sum: e9e221dfa2a3bdd95490634d192e6be2 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_13 git_last_commit: 4f9c531 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/exomeCopy_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/exomeCopy_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/exomeCopy_1.38.0.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: 29 Package: exomePeak2 Version: 1.4.2 Depends: SummarizedExperiment,cqn Imports: Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,genefilter,Biostrings,BSgenome,BiocParallel,IRanges,S4Vectors,reshape2,rtracklayer,apeglm,methods,stats,utils,Biobase,GenomeInfoDb,BiocGenerics Suggests: knitr, rmarkdown, RMariaDB License: GPL (>= 2) Archs: i386, x64 MD5sum: 60fb6521f21b22fa3917d8a4eac7a6d9 NeedsCompilation: no Title: Bias-aware Peak Calling and Quantification for MeRIP-Seq Description: exomePeak2 provides bias-aware quantification and peak detection for Methylated RNA immunoprecipitation sequencing data (MeRIP-Seq). MeRIP-Seq is a commonly applied sequencing technology that can measure the location and abundance of RNA modification sites under given cell line conditions. However, quantification and peak calling in MeRIP-Seq are sensitive to PCR amplification biases, which generally present in next-generation sequencing (NGS) technologies. In addition, the count data generated by RNA-Seq exhibits significant biological variations between biological replicates. exomePeak2 collectively address the challenges by introducing a series of robust data science tools tailored for MeRIP-Seq. Using exomePeak2, users can perform peak calling, modification site quantification and differential analysis through a straightforward single-step function. Alternatively, multi-step functions can be used to generate diagnostic plots and perform customized analyses. biocViews: Sequencing, MethylSeq, RNASeq, ExomeSeq, Coverage, Normalization, Preprocessing, DifferentialExpression 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_13 git_last_commit: d8d75f9 git_last_commit_date: 2021-09-07 Date/Publication: 2021-09-09 source.ver: src/contrib/exomePeak2_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/exomePeak2_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/exomePeak2_1.4.2.tgz vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.html vignetteTitles: The exomePeak2 user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.R dependencyCount: 138 Package: ExperimentHub Version: 2.0.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.19.3), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle, rmarkdown Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: 764124b06c2f9c00bce62f59f0a1fc18 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_13 git_last_commit: a899441 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExperimentHub_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentHub_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHub_2.0.0.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, brainImageRdata, CellMapperData, clustifyrdatahub, curatedAdipoChIP, DMRcatedata, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData, NanoporeRNASeq, NestLink, ObMiTi, restfulSEData, RNAmodR.Data, SCATEData, scpdata, sesameData, SimBenchData, STexampleData, tartare, tcgaWGBSData.hg19, TENxVisiumData importsMe: BloodGen3Module, DMRcate, EWCE, ExperimentHubData, GSEABenchmarkeR, MACSr, PhyloProfile, restfulSE, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, emtdata, FieldEffectCrc, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, imcdatasets, LRcellTypeMarkers, methylclockData, MethylSeqData, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, msigdb, PhyloProfileData, preciseTADhub, pwrEWAS.data, scRNAseq, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TENxBrainData, TENxBUSData, TENxPBMCData suggestsMe: ANF, AnnotationHub, bambu, celaref, CellMapper, HDF5Array, missMethyl, muscat, quantiseqr, rawrr, recountmethylation, SingleMoleculeFootprinting, celarefData, curatedAdipoArray, GSE13015, GSE62944, tissueTreg dependencyCount: 87 Package: ExperimentHubData Version: 1.18.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 30ce148573af2a3a62ceae0bd3e41b45 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_13 git_last_commit: 2706bf5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExperimentHubData_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentHubData_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHubData_1.18.0.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: 135 Package: ExperimentSubset Version: 1.2.0 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 Archs: i386, x64 MD5sum: 20eb1c88f4c077fb178634127d7126ec 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_13 git_last_commit: f9c08bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExperimentSubset_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentSubset_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentSubset_1.2.0.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: 104 Package: ExploreModelMatrix Version: 1.4.0 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: 2b85533c9714cf15e7af6744b91640b8 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_13 git_last_commit: 72ccfe4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExploreModelMatrix_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExploreModelMatrix_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExploreModelMatrix_1.4.0.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: 1.20.0 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: cae510f42f7b488ce88491aa1d31de7d 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: Suhaib Mohammed VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_13 git_last_commit: d1a0181 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ExpressionAtlas_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExpressionAtlas_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExpressionAtlas_1.20.0.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: 37 Package: fabia Version: 2.38.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: f5e75566976f6eab225bd37e6ba8073f 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_13 git_last_commit: 7a5d2e0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fabia_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fabia_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fabia_2.38.0.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, BcDiag, CSFA suggestsMe: fabiaData dependencyCount: 8 Package: factDesign Version: 1.68.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: c8121e48d2a0efdb3589c0674e2b33c3 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_13 git_last_commit: c4ad08e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/factDesign_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/factDesign_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/factDesign_1.68.0.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: 7 Package: FamAgg Version: 1.20.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 2b53af2b66372d047d0c86a5e7b8a9a8 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_13 git_last_commit: b92025e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FamAgg_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FamAgg_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FamAgg_1.20.0.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: 24 Package: famat Version: 1.2.1 Depends: R (>= 4.0) Imports: KEGGREST, MPINet, 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: 4167fb34f2b660a09aface39f4df9014 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_13 git_last_commit: f97cf00 git_last_commit_date: 2021-10-13 Date/Publication: 2021-10-14 source.ver: src/contrib/famat_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/famat_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/famat_1.2.1.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: 1.44.0 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) Archs: i386, x64 MD5sum: ad8a9a58915131a7ec16f0c03749df28 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_13 git_last_commit: 9cf9a17 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/farms_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/farms_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/farms_1.44.0.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: 14 Package: fastLiquidAssociation Version: 1.28.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 5bd4dc9aabf1e12094aec321de1473ff 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_13 git_last_commit: a7ffa47 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fastLiquidAssociation_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fastLiquidAssociation_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fastLiquidAssociation_1.28.0.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: 120 Package: FastqCleaner Version: 1.10.0 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: aded4c4f79045ba406c4c5c3a7e74687 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_13 git_last_commit: 6704c61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FastqCleaner_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FastqCleaner_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FastqCleaner_1.10.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.pdf vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 78 Package: fastseg Version: 1.38.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) MD5sum: 999e079f094df90dae22ca0daf924bea 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_13 git_last_commit: 48225db git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fastseg_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fastseg_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fastseg_1.38.0.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: 18 Package: FCBF Version: 2.0.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE MD5sum: d5f13ebb2e43fec151f3c581d3ec2532 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_13 git_last_commit: 3eb0809 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FCBF_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FCBF_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FCBF_2.0.0.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: 59 Package: fCCAC Version: 1.18.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 4db63f8697305b627ad47eed801b855d NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). The package can be used as well with other types of sequencing data such as neMeRIP-seq (see PMID: 29489750). biocViews: Transcription, Genetics, Sequencing, Coverage Author: Pedro Madrigal Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_13 git_last_commit: 9de3dbd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fCCAC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fCCAC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fCCAC_1.18.0.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: 129 Package: fCI Version: 1.22.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 9f321c2be0500454610077f00dcb62c6 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_13 git_last_commit: 0a5f069 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fCI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fCI_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fCI_1.22.0.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: 41 Package: fcoex Version: 1.6.0 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 License: GPL-3 Archs: i386, x64 MD5sum: 19bd35aab36bc21061e7d8d9485cac64 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_13 git_last_commit: f4e35ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fcoex_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fcoex_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fcoex_1.6.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 146 Package: fcScan Version: 1.6.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 4d86d14e5766b4dbbcfa627455e5b69c 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 Ghiwa khalil Georges Khazen Pierre Khoueiry Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_13 git_last_commit: 12424b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fcScan_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fcScan_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fcScan_1.6.0.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: 99 Package: fdrame Version: 1.64.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 91110a9757d76a174566e78331b2924d 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_13 git_last_commit: a59c8a8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fdrame_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fdrame_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fdrame_1.64.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.0.0 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 MD5sum: 4dfc32ee4a4fdc55ae626914af72619f 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_13 git_last_commit: f91b6e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FEAST_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FEAST_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FEAST_1.0.0.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: 116 Package: fedup Version: 1.0.0 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 MD5sum: 07b18908d560ebbff8f9f9eaf470b5d2 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_13 git_last_commit: 5bbca3c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fedup_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fedup_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fedup_1.0.0.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: 88 Package: FELLA Version: 1.12.0 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: 45ac501b0bf99b3cab0abdb5b22ba8d9 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_13 git_last_commit: a4fcd75 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FELLA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FELLA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FELLA_1.12.0.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: 1.36.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) Archs: i386, x64 MD5sum: 496a8d68f02467551fc345aa34e6b76a 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_13 git_last_commit: b02bf4a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ffpe_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ffpe_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ffpe_1.36.0.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: 164 Package: fgga Version: 1.0.0 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 Archs: i386, x64 MD5sum: 6045b89228ce41833449e74a641d2e67 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_13 git_last_commit: aefd8a8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fgga_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fgga_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fgga_1.0.0.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: 65 Package: FGNet Version: 3.26.0 Depends: R (>= 2.15) 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, RGtk2, BiocManager License: GPL (>= 2) MD5sum: 4f0eb2ca870f16d87d098f49e72950a1 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_13 git_last_commit: 11b6029 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FGNet_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FGNet_3.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FGNet_3.26.0.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: 26 Package: fgsea Version: 1.18.0 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: ec7032b1402576136e219873ca483fc0 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_13 git_last_commit: 9b6e7d0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fgsea_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fgsea_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fgsea_1.18.0.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, PPInfer importsMe: ASpediaFI, CelliD, CEMiTool, clustifyr, cTRAP, DOSE, fobitools, lipidr, mCSEA, multiGSEA, phantasus, piano, RegEnrich, signatureSearch, ViSEAGO, cinaR suggestsMe: mdp, Pi, ttgsea, rliger dependencyCount: 50 Package: FilterFFPE Version: 1.2.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: f8bc451b716e49cec6182f81a637ba45 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_13 git_last_commit: 567bb26 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FilterFFPE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FilterFFPE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FilterFFPE_1.2.0.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: FindMyFriends Version: 1.22.0 Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel, utils, stats LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils License: GPL (>=2) MD5sum: c6a295e68fe061694a38f29cb5e7038b NeedsCompilation: yes Title: Microbial Comparative Genomics in R Description: A framework for doing microbial comparative genomics in R. The main purpose of the package is assisting in the creation of pangenome matrices where genes from related organisms are grouped by similarity, as well as the analysis of these data. FindMyFriends provides many novel approaches to doing pangenome analysis and supports a gene grouping algorithm that scales linearly, thus making the creation of huge pangenomes feasible. biocViews: ComparativeGenomics, Clustering, DataRepresentation, GenomicVariation, SequenceMatching, GraphAndNetwork Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/FindMyFriends VignetteBuilder: knitr BugReports: https://github.com/thomasp85/FindMyFriends/issues git_url: https://git.bioconductor.org/packages/FindMyFriends git_branch: RELEASE_3_13 git_last_commit: 526b1e4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FindMyFriends_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/FindMyFriends_1.22.0.tgz vignettes: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html vignetteTitles: Creating pangenomes using FindMyFriends hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R importsMe: PanVizGenerator dependencyCount: 78 Package: FISHalyseR Version: 1.26.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 01359c93ea838a357e1e0af5dd1ba21a 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_13 git_last_commit: 1ac9d07 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FISHalyseR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FISHalyseR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FISHalyseR_1.26.0.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: 26 Package: fishpond Version: 1.8.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, SummarizedExperiment, matrixStats, svMisc, Rcpp, Matrix LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, SingleCellExperiment, limma License: GPL-2 MD5sum: 7112c4eb391dd1e1cf4ec2aa52e9baf4 NeedsCompilation: yes Title: Fishpond: differential transcript and gene expression with inferential replicates 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 utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anzi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Scott Van Buren [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_13 git_last_commit: 38a320c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fishpond_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fishpond_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fishpond_1.8.0.tgz vignettes: vignettes/fishpond/inst/doc/swish.html vignetteTitles: DTE and DGE with inferential replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/swish.R importsMe: singleCellTK suggestsMe: tximport dependencyCount: 65 Package: FitHiC Version: 1.18.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 2d33a8d5c1c1ce373888456b8ff0de29 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_13 git_last_commit: 62f951d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FitHiC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FitHiC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FitHiC_1.18.0.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: 1.48.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) MD5sum: 7dfcd3f44b0a4e346533964a5381cc71 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: ImmunoOncology, DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_13 git_last_commit: 31bd595 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flagme_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flagme_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flagme_1.48.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R dependencyCount: 132 Package: flowAI Version: 1.22.0 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) MD5sum: a146ce5cbfce13a8ab2bafa1bbe0d743 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_13 git_last_commit: a517a94 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowAI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowAI_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowAI_1.22.0.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: 71 Package: flowBeads Version: 1.30.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 Archs: i386, x64 MD5sum: 64f94f8bc1a076e4b39e124e6c49b8dc 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_13 git_last_commit: 57c2789 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowBeads_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowBeads_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowBeads_1.30.0.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: 37 Package: flowBin Version: 1.28.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 9145376a7390a2763cf7023d77c3d954 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_13 git_last_commit: 7e5e658 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowBin_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowBin_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowBin_1.28.0.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: 34 Package: flowcatchR Version: 1.26.0 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 Archs: i386, x64 MD5sum: c0acc9b3570a53503e50e19ef471e192 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_13 git_last_commit: 33b6f14 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowcatchR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowcatchR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowcatchR_1.26.0.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: 95 Package: flowCHIC Version: 1.26.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 3f341bc9080165fda378029034dd4961 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_13 git_last_commit: 16676cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowCHIC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCHIC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCHIC_1.26.0.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: 71 Package: flowCL Version: 1.30.0 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 402775cbb0ad31930b7f345762a00438 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_13 git_last_commit: f0a1525 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowCL_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCL_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCL_1.30.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 15 Package: flowClean Version: 1.30.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: c71ae0c9853448470d0661e98879a146 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_13 git_last_commit: 5e882c9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowClean_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowClean_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowClean_1.30.0.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: 26 Package: flowClust Version: 3.30.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, ellipse, flowViz, flowCore, clue, corpcor, mnormt, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto License: Artistic-2.0 MD5sum: 4a0016e67160417cf7ec4ab21e157e6a 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_13 git_last_commit: c52585b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowClust_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowClust_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowClust_3.30.0.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: flowTrans suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 37 Package: flowCore Version: 2.4.0 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 MD5sum: 278d033fc84e1dc292538889a0c154f3 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_13 git_last_commit: 1f5f4b6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowCore_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCore_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCore_2.4.0.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, 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, FlowRepositoryR, flowPloidyData, hypergate, segmenTier dependencyCount: 18 Package: flowCut Version: 1.2.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 9a572c44c8d40e4d34ebcfef9021ddc9 NeedsCompilation: no Title: Precise and Accurate 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_13 git_last_commit: 3edb4be git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowCut_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCut_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCut_1.2.0.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: 155 Package: flowCyBar Version: 1.28.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: e12d67586a089b3e438ced3625cf8354 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_13 git_last_commit: 1cec105 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowCyBar_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCyBar_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCyBar_1.28.0.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: 1.26.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 1fa498c3a8442991693352da0e8a8b03 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_13 git_last_commit: 6559b9f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowDensity_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowDensity_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowDensity_1.26.0.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: 150 Package: flowFP Version: 1.50.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 7f0f26448743300fc9dfdaf09cb0c258 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_13 git_last_commit: 4749abd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowFP_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowFP_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowFP_1.50.0.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: 30 Package: flowGraph Version: 1.0.0 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, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: fd5b95e6b09c4bb28c2beacc65a20959 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_13 git_last_commit: 00d2dcb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowGraph_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowGraph_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowGraph_1.0.0.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: 69 Package: flowMap Version: 1.30.0 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: 43bf2eb0add987c7b00669b6fa92de0b 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_13 git_last_commit: 040845e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowMap_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMap_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMap_1.30.0.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: 36 Package: flowMatch Version: 1.28.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 8ad623bf470412bb97ad5ac9fd8b5dd1 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_13 git_last_commit: 9bf49d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowMatch_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMatch_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMatch_1.28.0.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: 19 Package: flowMeans Version: 1.52.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: f005ac1b9db4a9b0c6282b14de8f4238 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_13 git_last_commit: eaf6792 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowMeans_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMeans_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMeans_1.52.0.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: 41 Package: flowMerge Version: 2.40.0 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: 08443b9702c260df0415fbaa54d3eb7f 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_13 git_last_commit: beb6e37 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowMerge_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMerge_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMerge_2.40.0.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: 62 Package: flowPeaks Version: 1.38.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 MD5sum: e9f07b362494a00a5ab14351da420cbf 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_13 git_last_commit: 214f2c7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowPeaks_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPeaks_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPeaks_1.38.0.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: 1.18.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 Archs: i386, x64 MD5sum: 9d7c100bdb9b4ad1df7f901e9286e970 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_13 git_last_commit: 565690e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowPloidy_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPloidy_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPloidy_1.18.0.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: 119 Package: flowPlots Version: 1.40.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 422de928885d653973b33babc12b1812 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_13 git_last_commit: 81be8c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowPlots_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPlots_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPlots_1.40.0.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: FlowRepositoryR Version: 1.23.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: 0718e29c9f61615b249a342c6adacca0 NeedsCompilation: no Title: FlowRepository R Interface Description: This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server. biocViews: ImmunoOncology, Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen git_url: https://git.bioconductor.org/packages/FlowRepositoryR git_branch: master git_last_commit: 83c2a9f git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-20 source.ver: src/contrib/FlowRepositoryR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FlowRepositoryR_1.23.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FlowRepositoryR_1.23.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R dependencyCount: 7 Package: FlowSOM Version: 2.0.0 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) Archs: i386, x64 MD5sum: 4f6840f09ab161355c5f1f8c10f77877 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_13 git_last_commit: 663dedc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FlowSOM_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FlowSOM_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FlowSOM_2.0.0.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: 193 Package: flowSpecs Version: 1.6.0 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) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: 037334f156cdd29cee2c50c4f22e35a0 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_13 git_last_commit: e8a3b4e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowSpecs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowSpecs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowSpecs_1.6.0.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: 4.4.0 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 Suggests: xtable, testthat, openCyto Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 8396614b53bfc338bd1ba5562e039cfa 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 , Jake Wagner 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_13 git_last_commit: 5d3758e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowStats_4.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowStats_4.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowStats_4.4.0.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, flowCore, flowTime, flowViz, ggcyto dependencyCount: 110 Package: flowTime Version: 1.16.1 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: b16c4e26f6b48cd1910f68415998efca 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_13 git_last_commit: 580e1b8 git_last_commit_date: 2021-07-27 Date/Publication: 2021-07-29 source.ver: src/contrib/flowTime_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowTime_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/flowTime_1.16.1.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: 38 Package: flowTrans Version: 1.44.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 Archs: i386, x64 MD5sum: 348699f440d4bd44be6d2221cc411a51 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_13 git_last_commit: 6d79ae3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowTrans_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowTrans_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowTrans_1.44.0.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: 38 Package: flowUtils Version: 1.56.0 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: dd39163bd7144dfd7c2ee6ca882a04fa 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: RELEASE_3_13 git_last_commit: ad93cd2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowUtils_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowUtils_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowUtils_1.56.0.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: 23 Package: flowViz Version: 1.56.0 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, testthat License: Artistic-2.0 MD5sum: 0a1a47cdacca2940b5836d4a14b23966 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 , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_13 git_last_commit: f90ec49 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowViz_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowViz_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowViz_1.56.0.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: flowClust, flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 29 Package: flowVS Version: 1.24.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 259d9e5f4823b8665572e8d2d67673ed 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_13 git_last_commit: 095b0d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowVS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowVS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowVS_1.24.0.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: 111 Package: flowWorkspace Version: 4.4.0 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, ggcyto, parallel, CytoML, openCyto License: file LICENSE License_restricts_use: yes MD5sum: 7c871faaf53cfca2e0d10ee9b6e81189 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_13 git_last_commit: 8b26faf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/flowWorkspace_4.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowWorkspace_4.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowWorkspace_4.4.0.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: 81 Package: fmcsR Version: 1.34.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown License: Artistic-2.0 MD5sum: b938405424e87121ae4b87d823fc2a18 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_13 git_last_commit: 9440089 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fmcsR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fmcsR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fmcsR_1.34.0.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: Rcpi, BioMedR suggestsMe: ChemmineR, xnet dependencyCount: 63 Package: fmrs Version: 1.2.0 Depends: R (>= 4.0.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL (>= 3) MD5sum: 65c15515082a303aa26587a03a494da2 NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Description: Provides parameter estimation as well as variable selection in Finite Mixture of Accelerated Failure Time Regression and Finite Mixture of Regression Models. 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_13 git_last_commit: 0aa7bb6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fmrs_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fmrs_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fmrs_1.2.0.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: 1.0.0 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 MD5sum: 571db4ec49549003ef7759653818ddf0 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_13 git_last_commit: ad1d5ca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/fobitools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fobitools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fobitools_1.0.0.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: 123 Package: FoldGO Version: 1.10.0 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 Archs: i386, x64 MD5sum: 686728ba47a787737b7283e54df9c68a 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_13 git_last_commit: defd37f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FoldGO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FoldGO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FoldGO_1.10.0.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: 83 Package: FRASER Version: 1.4.0 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 Archs: i386, x64 MD5sum: 554a5ead744a51a5e0f834d475b02e7e NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. The workflow aims to assist the 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_13 git_last_commit: 926f313 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FRASER_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FRASER_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FRASER_1.4.0.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: 1.4.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a65d618d00555195248eeaa508fc7652 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_13 git_last_commit: 1497b34 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/frenchFISH_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frenchFISH_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frenchFISH_1.4.0.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: 89 Package: FRGEpistasis Version: 1.28.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 6f9efdfdb9287727bb23ff7dba6d5ce4 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_13 git_last_commit: ed89200 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FRGEpistasis_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FRGEpistasis_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FRGEpistasis_1.28.0.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: 61 Package: frma Version: 1.44.0 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: e469fc86ed9a0935437ed932d3dd3ad0 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_13 git_last_commit: 6ef2453 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/frma_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frma_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frma_1.44.0.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: 56 Package: frmaTools Version: 1.44.0 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) Archs: i386, x64 MD5sum: 95f608947491aeeb4fbb14bdf2a67e34 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_13 git_last_commit: 619dae9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/frmaTools_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frmaTools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frmaTools_1.44.0.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: 14 Package: FScanR Version: 1.2.0 Depends: R (>= 4.0) Imports: stats Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: d14e03beeef0b37d6c7c800e79bd8d70 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_13 git_last_commit: 95f349c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FScanR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FScanR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FScanR_1.2.0.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: 1.18.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 MD5sum: 4c2fcc5aa80f023252527c297076c479 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_13 git_last_commit: 4981a2b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/FunChIP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FunChIP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FunChIP_1.18.0.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: 111 Package: funtooNorm Version: 1.16.0 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 MD5sum: 1e7baa5f935ebfa0f007e3f4b9d917d3 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_13 git_last_commit: 3256fea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/funtooNorm_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/funtooNorm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/funtooNorm_1.16.0.tgz vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k for multiple cell types with funtooNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R dependencyCount: 142 Package: GA4GHclient Version: 1.16.0 Depends: 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: 38b916d73a6254df6daa32459ffe099a 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_13 git_last_commit: d3644e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GA4GHclient_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GA4GHclient_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GA4GHclient_1.16.0.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: 98 Package: GA4GHshiny Version: 1.14.0 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: 345a18b4c923582fd8128fbc17b82288 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_13 git_last_commit: 8c2cb31 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GA4GHshiny_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GA4GHshiny_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GA4GHshiny_1.14.0.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: 123 Package: gaga Version: 2.38.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 0236bccc048ff84e802840e55c6bdd7d 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_13 git_last_commit: 91b7446 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gaga_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaga_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaga_2.38.0.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: 17 Package: gage Version: 2.42.0 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) MD5sum: f8ea31b7de2e654192c808cb19b4b084 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_13 git_last_commit: 8b222d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gage_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gage_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gage_2.42.0.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: 48 Package: gaggle Version: 1.60.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 47d9a991d7372940e9f2ae1fdb9d127b 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_13 git_last_commit: 9d24cfd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gaggle_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaggle_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaggle_1.60.0.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: 10 Package: gaia Version: 2.36.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 774fc52ac1c55f63730c406fa114e5fa 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: RELEASE_3_13 git_last_commit: 4524f5d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gaia_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaia_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaia_2.36.0.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 importsMe: TCGAWorkflow dependencyCount: 0 Package: GAPGOM Version: 1.8.0 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 Archs: i386, x64 MD5sum: 701fc46ae79289d4c314abb2873b8cb1 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: RELEASE_3_13 git_last_commit: e8e35ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GAPGOM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GAPGOM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GAPGOM_1.8.0.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: 90 Package: GAprediction Version: 1.18.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: f18abfea6e9c8270875f41bc70dbce65 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_13 git_last_commit: 5d46177 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GAprediction_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GAprediction_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GAprediction_1.18.0.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: 15 Package: garfield Version: 1.20.0 Suggests: knitr License: GPL-3 MD5sum: dfce0c090510b7af183487a54b5dadf1 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_13 git_last_commit: 67bb168 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/garfield_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/garfield_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/garfield_1.20.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.12.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 32e3573772e4fb0cc2a63e39e7c6c442 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_13 git_last_commit: 4d2039e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GARS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GARS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GARS_1.12.0.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: 246 Package: GateFinder Version: 1.12.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 16f5fb3288026bae5d59d592037ccb49 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_13 git_last_commit: fd719d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GateFinder_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GateFinder_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GateFinder_1.12.0.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: 38 Package: gcapc Version: 1.16.0 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 MD5sum: b6e014a1f85f383eb4aac4c188c8ba55 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_13 git_last_commit: 38a05b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gcapc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcapc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcapc_1.16.0.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: 47 Package: gcatest Version: 1.22.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: f1afbe1cdf1bb8c558694edda2f35c7c 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_13 git_last_commit: 9efedc7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gcatest_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcatest_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcatest_1.22.0.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: 1.20.0 Depends: R (>= 3.6) Imports: Biobase, limma, RobustRankAggreg, ggplot2, PANTHER.db, rmarkdown, grDevices, graphics, stats, utils, parallel, SummarizedExperiment Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a35d60a9e376918c4e31ff076ee11f35 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. 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_13 git_last_commit: ea57239 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gCrisprTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gCrisprTools_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gCrisprTools_1.20.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 120 Package: gcrma Version: 2.64.0 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: db8329cd91ac5ba2d25274000de3c947 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_13 git_last_commit: 9914de3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gcrma_2.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcrma_2.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcrma_2.64.0.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: 25 Package: GCSConnection Version: 1.4.0 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: 7d74056b9d1501f9598075b7910172aa 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: RELEASE_3_13 git_last_commit: 47d39d5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GCSConnection_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSConnection_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSConnection_1.4.0.tgz 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: 1.2.0 Depends: R (>= 4.0.0) Imports: stats Suggests: testthat, knitr, rmarkdown, BiocStyle, GCSConnection License: GPL (>= 2) Archs: i386, x64 MD5sum: 858728581e30dba2ded7a79b2216cece 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] Maintainer: Jiefei Wang SystemRequirements: GCSDokan for Windows, gcsfuse for Linux and macOs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSFilesystem git_branch: RELEASE_3_13 git_last_commit: 7594317 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GCSFilesystem_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSFilesystem_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSFilesystem_1.2.0.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: 1.6.0 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: b980acab414397876cfba022740f1773 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_13 git_last_commit: 8bfdd99 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GCSscore_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSscore_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSscore_1.6.0.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: 103 Package: GDCRNATools Version: 1.13.1 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 Archs: i386, x64 MD5sum: bfbb746a6f08b1d2703505d279fd6235 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_13 git_last_commit: 48bda50 git_last_commit_date: 2021-08-03 Date/Publication: 2021-08-03 source.ver: src/contrib/GDCRNATools_1.13.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GDCRNATools_1.13.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GDCRNATools_1.13.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R dependencyCount: 232 Package: GDSArray Version: 1.12.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, BiocStyle, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: 881a8dc6b05d40a441540814b5c58e2a 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_13 git_last_commit: f2a3983 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GDSArray_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GDSArray_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GDSArray_1.12.0.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 dependsOnMe: VariantExperiment importsMe: CNVRanger suggestsMe: DelayedDataFrame dependencyCount: 29 Package: gdsfmt Version: 1.28.1 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: 961a142b23fb40cfcf191859784f5e13 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_13 git_last_commit: c897242 git_last_commit_date: 2021-09-15 Date/Publication: 2021-09-16 source.ver: src/contrib/gdsfmt_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/gdsfmt_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/gdsfmt_1.28.1.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 importsMe: CNVRanger, GENESIS, GWASTools, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.18.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 MD5sum: 22372e7518ac68318371912f6b81b405 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_13 git_last_commit: 72dc859 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEM_1.18.0.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: 39 Package: gemini Version: 1.6.0 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: 619bbd6cbc7b6883cccef4810f492ffa 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_13 git_last_commit: bdea931 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gemini_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gemini_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gemini_1.6.0.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: 48 Package: genArise Version: 1.68.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 0b4e06d3d0208e643418cc48eb9f3f41 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_13 git_last_commit: b242c5b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genArise_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genArise_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genArise_1.68.0.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: 1.20.0 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 Archs: i386, x64 MD5sum: cffa22acf248d889d4725ffc0e57e61b 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_13 git_last_commit: 968c425 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genbankr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genbankr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genbankr_1.20.0.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: 98 Package: GeneAccord Version: 1.10.0 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: d4fb31c743ecd6b2592b90541142dd02 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_13 git_last_commit: cfc8768 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneAccord_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneAccord_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneAccord_1.10.0.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: 148 Package: geneAttribution Version: 1.18.0 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: 9061ceee02c52b0f67799c3914683396 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_13 git_last_commit: e7e4262 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneAttribution_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneAttribution_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneAttribution_1.18.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 97 Package: GeneBreak Version: 1.22.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 660e127ff979f9a565e21fbee35c1fc4 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_13 git_last_commit: 4c84ab8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneBreak_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneBreak_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneBreak_1.22.0.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: 1.16.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 36b2ff9a6bc89a20ba1804f7a86c1760 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_13 git_last_commit: 479c49c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneClassifiers_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneClassifiers_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneClassifiers_1.16.0.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: 7 Package: GeneExpressionSignature Version: 1.38.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 3dfb793015e720c6ac6766f9d92a86e2 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_13 git_last_commit: ed0ab84 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneExpressionSignature_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneExpressionSignature_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneExpressionSignature_1.38.0.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: 7 Package: genefilter Version: 1.74.1 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: d91770f2bde1d21328067709c16e0b19 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_13 git_last_commit: 3ca5b57 git_last_commit_date: 2021-08-19 Date/Publication: 2021-10-12 source.ver: src/contrib/genefilter_1.74.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/genefilter_1.74.1.zip mac.binary.ver: bin/macosx/contrib/4.1/genefilter_1.74.1.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, FlowSorted.Blood.EPIC, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM, orQA importsMe: a4Base, ALPS, annmap, arrayQualityMetrics, Category, cbaf, countsimQC, covRNA, DESeq2, DEXSeq, GISPA, GSRI, metaseqR2, methyAnalysis, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, pwrEWAS, Ringo, spatialHeatmap, tilingArray, XDE, zinbwave, IHWpaper, RNAinteractMAPK, CoNI, dGAselID, INCATome, MiDA, netgsa, oncoPredict, specmine suggestsMe: annotate, BioNet, categoryCompare, ClassifyR, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, rknn, SuperLearner dependencyCount: 54 Package: genefu Version: 2.24.2 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 MD5sum: 83739ed85019450d7ee834240ac741e5 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_13 git_last_commit: 829a30e git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-23 source.ver: src/contrib/genefu_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/genefu_2.24.2.zip mac.binary.ver: bin/macosx/contrib/4.1/genefu_2.24.2.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: 1.42.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: bef45aaeee0c715c264005645286e8f0 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_13 git_last_commit: afbbb4a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneGA_1.42.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/GeneGA_1.42.0.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: 14 Package: GeneGeneInteR Version: 1.18.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 130dabb7324b4652fd8565c63a270272 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_13 git_last_commit: c3d7539 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneGeneInteR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneGeneInteR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneGeneInteR_1.18.0.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: 139 Package: GeneMeta Version: 1.64.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 203e065b502a46c2476c7b4124b5c592 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_13 git_last_commit: 24685d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneMeta_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneMeta_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneMeta_1.64.0.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: 55 Package: GeneNetworkBuilder Version: 1.34.0 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) Archs: i386, x64 MD5sum: ce3e74db5e3c292883abeaebf2ff228e 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_13 git_last_commit: 2e9cc20 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneNetworkBuilder_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneNetworkBuilder_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneNetworkBuilder_1.34.0.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: 23 Package: GeneOverlap Version: 1.28.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 56db0bcbac4b22d03a1ef391e1df86a7 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_13 git_last_commit: 870f8c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneOverlap_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneOverlap_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneOverlap_1.28.0.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: 1.18.0 Depends: R (>= 3.3), 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) Archs: i386, x64 MD5sum: 2f248f65322d91bbc9aa7d7981755e31 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_13 git_last_commit: d932944 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneplast_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneplast_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneplast_1.18.0.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 dependencyCount: 18 Package: geneplotter Version: 1.70.0 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: 3cebdd28df88565dc31a3e0d03c76023 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_13 git_last_commit: ca484d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneplotter_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneplotter_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneplotter_1.70.0.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: 52 Package: geneRecommender Version: 1.64.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 89008ca69a800e78e66f6dcfc31f3046 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_13 git_last_commit: 5e408e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneRecommender_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneRecommender_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneRecommender_1.64.0.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: 7 Package: GeneRegionScan Version: 1.48.0 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: 3aeba7b7bf14ffadea923bc2254fba5d 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_13 git_last_commit: 8331f9a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneRegionScan_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneRegionScan_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneRegionScan_1.48.0.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: 22 Package: geneRxCluster Version: 1.28.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: e2ede86e7259a5ff313e8e2528f04210 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_13 git_last_commit: b17ecd9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneRxCluster_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneRxCluster_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneRxCluster_1.28.0.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: 17 Package: GeneSelectMMD Version: 2.36.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) MD5sum: a63f71c5f816e570416371e8fb5d281d 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_13 git_last_commit: c265d5d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneSelectMMD_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneSelectMMD_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneSelectMMD_2.36.0.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: 10 Package: GENESIS Version: 2.22.2 Imports: Biobase, BiocGenerics, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, foreach, 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 MD5sum: 5704257e71e1ebbf547feb2ea23c6b7b 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_13 git_last_commit: df77222 git_last_commit_date: 2021-06-16 Date/Publication: 2021-06-17 source.ver: src/contrib/GENESIS_2.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/GENESIS_2.22.2.zip mac.binary.ver: bin/macosx/contrib/4.1/GENESIS_2.22.2.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: 89 Package: GeneStructureTools Version: 1.12.0 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 MD5sum: 7c2e9c96c453738905d0d78a6d119e55 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_13 git_last_commit: 1b05c47 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneStructureTools_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneStructureTools_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneStructureTools_1.12.0.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: 145 Package: geNetClassifier Version: 1.32.0 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) Archs: i386, x64 MD5sum: 1c38e7c17af3edfeca098bacbf0e338d 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_13 git_last_commit: 1f86845 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geNetClassifier_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geNetClassifier_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geNetClassifier_1.32.0.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: 18 Package: GeneticsPed Version: 1.54.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: 39c0c97194d14cc2d206256a98280723 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_13 git_last_commit: aa226f9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeneticsPed_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneticsPed_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneticsPed_1.54.0.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: 1.4.1 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, 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, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 8660d111876085abe4e210006e6d072a 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_13 git_last_commit: 32c4e77 git_last_commit_date: 2021-06-04 Date/Publication: 2021-06-06 source.ver: src/contrib/GeneTonic_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneTonic_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneTonic_1.4.1.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: 170 Package: geneXtendeR Version: 1.18.0 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: 1802c9c8061c290f84779ea54c623e37 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_13 git_last_commit: 5f7ff0e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geneXtendeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneXtendeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneXtendeR_1.18.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 109 Package: GENIE3 Version: 1.14.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) MD5sum: 025689b6eda7f51baf356ae78eb1c3a4 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_13 git_last_commit: a186e5b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GENIE3_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GENIE3_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GENIE3_1.14.0.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 dependencyCount: 28 Package: genoCN Version: 1.44.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: 4f9d0f5d356f87eea0903609f2428737 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_13 git_last_commit: 22f18c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genoCN_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genoCN_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genoCN_1.44.0.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: GenoGAM Version: 2.10.0 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 MD5sum: d08d8bd8afaebfb5a0354bd852a82c73 NeedsCompilation: yes 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 git_url: https://git.bioconductor.org/packages/GenoGAM git_branch: RELEASE_3_13 git_last_commit: 3b86b0c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenoGAM_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenoGAM_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenoGAM_2.10.0.tgz vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.html vignetteTitles: "Modeling ChIP-Seq data with GenoGAM 2.0: A Genome-wide generalized additive model" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R dependencyCount: 104 Package: genomation Version: 1.24.0 Depends: R (>= 3.0.0),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: i386, x64 MD5sum: 520aa98cc953e739dabbeda4f3585672 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_13 git_last_commit: 94f0278 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genomation_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomation_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomation_1.24.0.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: fCCAC, RCAS suggestsMe: methylKit dependencyCount: 97 Package: GenomeInfoDb Version: 1.28.4 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: i386, x64 MD5sum: cee24e8eb4d5dfb1f8ead38f9e1f2c3f 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_13 git_last_commit: 3de2a41 git_last_commit_date: 2021-09-03 Date/Publication: 2021-09-05 source.ver: src/contrib/GenomeInfoDb_1.28.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomeInfoDb_1.28.4.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomeInfoDb_1.28.4.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, methyAnalysis, Rsamtools, SCOPE, VariantAnnotation, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, RTIGER importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, BaalChIP, ballgown, bambu, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, CAGEr, casper, cBioPortalData, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, customProDB, DAMEfinder, dasper, decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enrichTF, ensembldb, ensemblVEP, epialleleR, epigenomix, epigraHMM, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, FRASER, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, genotypeeval, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiCBricks, HiTC, HTSeqGenie, idr2d, IMAS, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene, metagene2, metaseqR2, metavizr, MethCP, methimpute, methInheritSim, methylKit, methylPipe, methylSig, methylumi, minfi, MinimumDistance, MMAPPR2, mosaics, motifbreakR, motifmatchr, msgbsR, multicrispr, multiHiCcompare, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rcade, RCAS, RcisTarget, recount, recoup, regioneR, regionReport, REMP, Repitools, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqsetvis, sevenC, SGSeq, ShortRead, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, TAPseq, TarSeqQC, TCGAutils, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TSRchitect, 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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.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, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, GenomicDistributionsData, MethylSeqData, TCGAWorkflow, ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR, HiCfeat, ICAMS, intePareto, MAAPER, MicroSEC, Signac, simMP suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch, ExperimentHubData, megadepth, methrix, MungeSumstats, parglms, QDNAseq, splatter, TFutils, gkmSVM, LDheatmap, polyRAD, Seurat dependencyCount: 12 Package: genomeIntervals Version: 1.48.0 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 Archs: i386, x64 MD5sum: 90d47a6399cd1fcb6386e1e66b6d6d41 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_13 git_last_commit: ab17e64 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genomeIntervals_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomeIntervals_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomeIntervals_1.48.0.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: 18 Package: genomes Version: 3.22.0 Depends: readr, curl License: GPL-3 Archs: i386, x64 MD5sum: 6191949054a209521bf925f35177108d 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_13 git_last_commit: ea29173 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genomes_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomes_3.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomes_3.22.0.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: 33 Package: GenomicAlignments Version: 1.28.0 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 Archs: i386, x64 MD5sum: 620d245c87edd17f00b589bd3661040b 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_13 git_last_commit: e755dc1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicAlignments_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicAlignments_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicAlignments_1.28.0.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, 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, FRASER, FunChIP, gcapc, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylPipe, mosaics, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, ramwas, Rcade, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, SCATE, scruff, seqsetvis, SGSeq, soGGi, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq, TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect, Ularcirc, UMI4Cats, VaSP, VplotR, leeBamViews, alakazam, BinQuasi, ExomeDepth, intePareto, MAAPER, MicroSEC, PACVr, pulseTD, RAPIDR, VALERIE suggestsMe: amplican, BiocParallel, csaw, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR, alpineData, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 37 Package: GenomicDataCommons Version: 1.16.0 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment, S4Vectors, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools License: Artistic-2.0 MD5sum: a3e66a51579b75e25df9ea96adc2a989 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] 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_13 git_last_commit: d614d6e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicDataCommons_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicDataCommons_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicDataCommons_1.16.0.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R importsMe: GDCRNATools, TCGAutils dependencyCount: 64 Package: GenomicDistributions Version: 1.0.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: c87f17315d1283d946a0108561b6bd6f NeedsCompilation: no Title: Produces Summaries and Plots of Features Distributed Across Genomes 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: Nathan C. Sheffield [aut], Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [ctb], John Lawson [ctb], Jose Verdezoto [ctb], Michal Stolarczyk [ctb], Jason Smith [ctb] 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_13 git_last_commit: 84c4876 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicDistributions_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicDistributions_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicDistributions_1.0.0.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: 58 Package: GenomicFeatures Version: 1.44.2 Depends: 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, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: a61f95ef6763f05d3ee72eff5386580e 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_13 git_last_commit: 49566bc git_last_commit_date: 2021-08-26 Date/Publication: 2021-08-26 source.ver: src/contrib/GenomicFeatures_1.44.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicFeatures_1.44.2.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicFeatures_1.44.2.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: 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.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.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.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, CompGO, consensusDE, crisprseekplus, CSSQ, customProDB, dasper, decompTumor2Sig, derfinder, derfinderPlot, EDASeq, ELMER, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, FRASER, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gmoviz, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, InPAS, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, metaseqR2, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, proActiv, proBAMr, PureCN, qpgraph, QuasR, RCAS, recoup, Rhisat2, RiboProfiling, ribosomeProfilingQC, RNAmodR, scruff, SGSeq, sitadela, SplicingGraphs, SPLINTER, srnadiff, StructuralVariantAnnotation, systemPipeR, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, 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, systemPipeRdata, driveR, MAAPER, oncoPredict, pathwayTMB, pulseTD, utr.annotation suggestsMe: AnnotationHub, BANDITS, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, pageRank, recount, RNAmodR.ML, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, 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, CAGEWorkflow, polyRAD dependencyCount: 95 Package: GenomicFiles Version: 1.28.0 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 Archs: i386, x64 MD5sum: 5906016debc92564a2124a8f4ddf6d24 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_13 git_last_commit: b7b64cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicFiles_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicFiles_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicFiles_1.28.0.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: 98 Package: GenomicInteractions Version: 1.26.0 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: d887f24e83af924d141a31a843802d1d 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_13 git_last_commit: 54f6f7e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicInteractions_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicInteractions_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicInteractions_1.26.0.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 suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 144 Package: GenomicOZone Version: 1.6.0 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: d4fae8b0c8adb3a200caa388d321cd14 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_13 git_last_commit: a1e58fc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicOZone_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicOZone_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicOZone_1.6.0.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: 157 Package: GenomicRanges Version: 1.44.0 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: 83b2127e657caef536d509904470cce6 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_13 git_last_commit: d27fdc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicRanges_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicRanges_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicRanges_1.44.0.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, 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, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet, esATAC, ExCluster, exomeCopy, fastseg, fCCAC, FunChIP, GeneBreak, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores, GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HiTC, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, maser, MBASED, Melissa, metagene, metagene2, methimpute, methyAnalysis, methylKit, methylPipe, minfi, MotifDb, msgbsR, MutationalPatterns, NADfinder, ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, ramr, Rcade, recoup, regioneR, RepViz, rfPred, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, SCOPE, segmentSeq, seqbias, seqCAT, SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, StructuralVariantAnnotation, SummarizedExperiment, TarSeqQC, TnT, trackViewer, TransView, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, 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, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver, sequencing, HiCfeat, PlasmaMutationDetector, RTIGER importsMe: ACE, ALDEx2, alpine, ALPS, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, BadRegionFinder, ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq, brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse, CAGEr, cBioPortalData, ChIC, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, CNEr, CNVfilteR, CNViz, coMET, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprseekplus, CrispRVariants, customProDB, DAMEfinder, dasper, debrowser, decompTumor2Sig, DeepBlueR, DEFormats, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffloop, diffUTR, DMRcate, dmrseq, DominoEffect, DRIMSeq, easyRNASeq, EDASeq, eisaR, ELMER, enrichTF, epialleleR, epidecodeR, epigraHMM, EpiTxDb, epivizr, epivizrData, erma, EventPointer, fcScan, FilterFFPE, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR, ggbio, GOfuncR, gpart, gwascat, h5vc, heatmaps, 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, MethCP, methInheritSim, MethReg, methrix, methyAnalysis, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, MMAPPR2, MMDiff2, Modstrings, mosaics, motifbreakR, motifmatchr, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, NanoMethViz, ncRNAtools, nearBynding, normr, nucleR, 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, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, RcisTarget, recount, recount3, regioneR, regionReport, regutools, REMP, Repitools, rGADEM, RGMQL, Rhisat2, RiboDiPA, RiboProfiling, RIPAT, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, RTCGAToolbox, SCATE, scmeth, scoreInvHap, scPipe, scruff, scuttle, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, snapcount, soGGi, SparseSignatures, SpectralTAD, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, tLOH, tracktables, transcriptR, transite, trena, tricycle, triplex, tscR, TSRchitect, TVTB, tximeta, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering, VaSP, VCFArray, wiggleplotr, 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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.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, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, COSMIC.67, ELMER.data, GenomicDistributionsData, leeBamViews, MethylSeqData, pepDat, scRNAseq, SomaticCancerAlterations, systemPipeRdata, VariantToolsData, recountWorkflow, TCGAWorkflow, ActiveDriverWGS, BinQuasi, cinaR, crispRdesignR, DGEobj, driveR, ExomeDepth, geno2proteo, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, noisyr, oncoPredict, PACVr, pagoo, RapidoPGS, RAPIDR, Signac, simMP, utr.annotation, VALERIE suggestsMe: AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, Chicago, CNVgears, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, maftools, MiRaGE, omicsPrint, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter, TFutils, universalmotif, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, sesameData, Single.mTEC.Transcriptomes, CAGEWorkflow, cancerTiming, chicane, gkmSVM, LDheatmap, polyRAD, rliger, seqmagick, Seurat, sigminer, valr dependencyCount: 16 Package: GenomicScores Version: 2.4.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, 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 MD5sum: 8a95a891e0b7f595aebe6f2cc452906a 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_13 git_last_commit: 4b46561 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicScores_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicScores_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicScores_2.4.0.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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons7way.UCSC.hg38 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 99 Package: GenomicSuperSignature Version: 1.0.1 Depends: R (>= 4.0), 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: 56b9c9c0590fe04e944998875595f532 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package contains the index, which is the Replicable and interpretable Axes of Variation (RAV) extracted from public RNA sequencing datasets by clustering and averaging top PCs. This index, named as RAVindex, is further annotated with MeSH terms and GSEA. Functions to connect PCs from new datasets to RAVs, extract interpretable annotations, and provide intuitive visualization, are implemented in this package. Overall, this package enables researchers to analyze new data in the context of existing databases with minimal computing resources. 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_13 git_last_commit: 56891d8 git_last_commit_date: 2021-05-27 Date/Publication: 2021-05-27 source.ver: src/contrib/GenomicSuperSignature_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicSuperSignature_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicSuperSignature_1.0.1.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: 168 Package: GenomicTuples Version: 1.26.0 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 License: Artistic-2.0 MD5sum: 3cae20797e55517ad4131529d965d34b 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_13 git_last_commit: c82b526 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenomicTuples_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicTuples_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicTuples_1.26.0.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: 19 Package: genotypeeval Version: 1.24.0 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: i386, x64 MD5sum: 7416170799a7ed18d8cc7ad60d887bff 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_13 git_last_commit: ddb6801 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genotypeeval_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genotypeeval_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genotypeeval_1.24.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 112 Package: genphen Version: 1.20.0 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: bf07f905cfe6b5d6240a063b1e4b6f9a NeedsCompilation: no Title: A tool for quantification of associations between genotypes and phenotypes in genome wide association studies (GWAS) with Bayesian inference and statistical learning Description: Genetic association studies are an essential tool for studying the relationship between genotypes and phenotypes. With genphen we can jointly study multiple phenotypes of different types, by quantifying the association between different genotypes and each phenotype using a hybrid method which uses statistical learning techniques such as random forest and support vector machines, and Bayesian inference using hierarchical models. 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 git_url: https://git.bioconductor.org/packages/genphen git_branch: RELEASE_3_13 git_last_commit: 33793d8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/genphen_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/genphen_1.20.0.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: 88 Package: GenVisR Version: 1.24.0 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: 6107719fa59f2b1631511b0791b2cbee 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_13 git_last_commit: 3b6abcf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GenVisR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenVisR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenVisR_1.24.0.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: 119 Package: GEOfastq Version: 1.0.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: c2e4b95297e2d0ac89118fef8e1f8551 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_13 git_last_commit: 8f1f5a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEOfastq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOfastq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOfastq_1.0.0.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: 1.54.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 Archs: i386, x64 MD5sum: 5b86974bae4844367b4516b20fb0b405 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_13 git_last_commit: db8e760 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEOmetadb_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOmetadb_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOmetadb_1.54.0.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: 57 Package: GeomxTools Version: 1.0.0 Depends: R (>= 3.6), NanoStringNCTools Imports: Biobase, S4Vectors, rjson, readxl, EnvStats, reshape2, methods, utils, stats, BiocGenerics Suggests: knitr License: Artistic-2.0 MD5sum: 051b01b21b078ecd52e8704e9ae79dc8 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 Author: Nicole Ortogero [cre, aut], Zhi Yang [aut] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: RELEASE_3_13 git_last_commit: 2462584 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GeomxTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeomxTools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeomxTools_1.0.0.tgz vignettes: vignettes/GeomxTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringGeomxSet Class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeomxTools/inst/doc/Introduction.R dependencyCount: 83 Package: GEOquery Version: 2.60.0 Depends: methods, Biobase Imports: httr, readr (>= 1.3.1), xml2, dplyr, tidyr, magrittr, limma Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr License: GPL-2 MD5sum: 461364700733ca25ce233a8df5dce4c5 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 Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_13 git_last_commit: 028b84d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEOquery_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOquery_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOquery_2.60.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE13015, GSE62944 importsMe: bigmelon, ChIPXpress, coexnet, conclus, crossmeta, DExMA, EGAD, GAPGOM, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb, BeadArrayUseCases, GSE13015, geneExpressionFromGEO, MetaIntegrator suggestsMe: AUCell, autonomics, ctsGE, dearseq, debCAM, diffcoexp, dyebias, EpiDISH, fgsea, GCSscore, GeneExpressionSignature, GenomicOZone, 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, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray dependencyCount: 48 Package: GEOsubmission Version: 1.44.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 932c3ad78f0555729d76ebca99f3f774 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_13 git_last_commit: 13a400d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEOsubmission_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOsubmission_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOsubmission_1.44.0.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: 13 Package: gep2pep Version: 1.12.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: adc093b16246abddcd4ed0e92ca96d64 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_13 git_last_commit: c8755ce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gep2pep_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gep2pep_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gep2pep_1.12.0.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: 59 Package: gespeR Version: 1.24.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: 6216e693b372313a4b4a51194cb738fd 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_13 git_last_commit: 59abb7c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gespeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gespeR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gespeR_1.24.0.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: 116 Package: getDEE2 Version: 1.2.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat License: GPL-3 MD5sum: 14e01f4f1e81fc59ae5e3dafe3b4d3d4 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_13 git_last_commit: 2536232 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/getDEE2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/getDEE2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/getDEE2_1.2.0.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: 27 Package: geva Version: 1.0.0 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: d9dfe0e9a52b3ae1659fb0156e18685c 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_13 git_last_commit: 7da51a4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/geva_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geva_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geva_1.0.0.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: 1.36.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 916f322c43b76fbbeec46b759eb7e22d 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_13 git_last_commit: 7171d51 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GEWIST_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEWIST_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEWIST_1.36.0.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: 84 Package: ggbio Version: 1.40.0 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: 6bfe0650246f6202b346dab2235c17b3 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: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_13 git_last_commit: c084632 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ggbio_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggbio_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggbio_1.40.0.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: derfinderPlot, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures suggestsMe: bambu, beadarray, ensembldb, gwascat, interactiveDisplay, NanoStringNCTools, Pi, regionReport, RnBeads, StructuralVariantAnnotation, universalmotif, IHWpaper, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 152 Package: ggcyto Version: 1.20.0 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: 648c64766259d7e55697236ed0ccf1a5 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 ,Jake Wagner URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_13 git_last_commit: 0fbbab2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ggcyto_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggcyto_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggcyto_1.20.0.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, flowTime, flowWorkspace, openCyto dependencyCount: 85 Package: GGPA Version: 1.4.0 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: 513113d9aecaa0cc2a94344a5d20780f 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_13 git_last_commit: c9bd582 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GGPA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GGPA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GGPA_1.4.0.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: ggtree Version: 3.0.4 Depends: R (>= 3.5.0) Imports: ape, aplot (>= 0.0.4), dplyr, ggfun, ggplot2 (>= 3.0.0), grid, magrittr, methods, purrr, rlang, scales, tidyr, tidytree (>= 0.2.6), treeio (>= 1.8.0), utils, yulab.utils Suggests: emojifont, ggimage, ggplotify, grDevices, knitr, prettydoc, rmarkdown, stats, testthat, tibble License: Artistic-2.0 MD5sum: f5d673cd08c69902749f614e3df29117 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] (), Yonghe Xia [ctb], Justin Silverman [ctb], Bradley Jones [ctb], Watal M. Iwasaki [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/treedata-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_13 git_last_commit: 7a83be2 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/ggtree_3.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggtree_3.0.4.zip mac.binary.ver: bin/macosx/contrib/4.1/ggtree_3.0.4.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 importsMe: enrichplot, ggtreeExtra, LymphoSeq, miaViz, MicrobiotaProcess, philr, singleCellTK, sitePath, systemPipeTools, treekoR, dowser, genBaRcode, harrietr, RAINBOWR, RevGadgets, STraTUS suggestsMe: systemPipeShiny, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE, deeptime, ggimage, idiogramFISH, microeco, nosoi, oppr, PCMBase, rhierbaps, tidytree, yatah dependencyCount: 58 Package: ggtreeExtra Version: 1.2.3 Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: bef4ad95448ba51cf6acc49034ead29c 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_13 git_last_commit: 759f5d0 git_last_commit_date: 2021-09-09 Date/Publication: 2021-09-12 source.ver: src/contrib/ggtreeExtra_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggtreeExtra_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.1/ggtreeExtra_1.2.3.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 suggestsMe: MicrobiotaProcess dependencyCount: 60 Package: GIGSEA Version: 1.10.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: f66a2cafbc1fbddd6e2c5d66988af89a 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_13 git_last_commit: ae9c9f8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GIGSEA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GIGSEA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GIGSEA_1.10.0.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: 1.44.0 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 Archs: i386, x64 MD5sum: 91a0c7ecac5cfe40a25870097cd646f7 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_13 git_last_commit: e753ae6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/girafe_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/girafe_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/girafe_1.44.0.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: 46 Package: GISPA Version: 1.16.0 Depends: R (>= 3.5) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 MD5sum: 2d9b35c05d15df3c4800e153bc268c97 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_13 git_last_commit: 5f33fd2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GISPA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GISPA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GISPA_1.16.0.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: 133 Package: GLAD Version: 2.56.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: 08df9292594a8073b825fecd9b9ae3c5 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_13 git_last_commit: 10516c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GLAD_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GLAD_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GLAD_2.56.0.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: 1.8.0 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 MD5sum: d19940d6ccf69caf8acdd20c976a171a 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_13 git_last_commit: 53cee0f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GladiaTOX_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GladiaTOX_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GladiaTOX_1.8.0.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: 68 Package: Glimma Version: 2.2.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr License: GPL-3 Archs: i386, x64 MD5sum: f65228a08cf29e34d4e5023ed60d5856 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_13 git_last_commit: 56d8c66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Glimma_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Glimma_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Glimma_2.2.0.tgz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html vignetteTitles: DESeq2, limma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools, EGSEA dependencyCount: 99 Package: glmGamPoi Version: 1.4.0 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: 6f716eda9f18a96d00c6ab5faee2c7a1 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_13 git_last_commit: 908a0c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/glmGamPoi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/glmGamPoi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/glmGamPoi_1.4.0.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 suggestsMe: DESeq2 dependencyCount: 36 Package: glmSparseNet Version: 1.10.0 Depends: R (>= 4.1), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, biomaRt, futile.logger, sparsebn, sparsebnUtils, forcats, dplyr, glue, readr, httr, ggplot2, survminer, reshape2, stringr, parallel, methods, loose.rock (>= 1.0.12) Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL-3 Archs: i386, x64 MD5sum: 2a1e7e6d2f124bbbf161da43f8c86cbc 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_13 git_last_commit: 68fb0a1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/glmSparseNet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/glmSparseNet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/glmSparseNet_1.10.0.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: 176 Package: GlobalAncova Version: 4.10.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: a2d1285c323a4b0c116361f3d0194f0d 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_13 git_last_commit: 4854ac8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GlobalAncova_4.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GlobalAncova_4.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GlobalAncova_4.10.0.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: 85 Package: globalSeq Version: 1.20.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 5951d09967083f56e10abc9464a399df 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_13 git_last_commit: d7fa5ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/globalSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/globalSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/globalSeq_1.20.0.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: 5.46.0 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) Archs: i386, x64 MD5sum: 466317f589d669ae7529b2ecbe85faf1 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_13 git_last_commit: 8595301 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/globaltest_5.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/globaltest_5.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/globaltest_5.46.0.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, maGUI, penalized dependencyCount: 54 Package: gmapR Version: 1.34.0 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: 8193c61b9bc6e98f460473a43749e82c 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_13 git_last_commit: 3262a4e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gmapR_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/gmapR_1.34.0.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: 98 Package: GmicR Version: 1.6.0 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: cd1722cc6b0e0cba22a4db1e44fd0cb0 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_13 git_last_commit: 1f79941 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GmicR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GmicR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GmicR_1.6.0.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: 147 Package: gmoviz Version: 1.4.0 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 Archs: i386, x64 MD5sum: 80ea379a6356969e3aafa8654cf2000b 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_13 git_last_commit: 8eb5807 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gmoviz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gmoviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gmoviz_1.4.0.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: 112 Package: GMRP Version: 1.20.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: 7ea3012cd2f049b296f04b05b3727998 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_13 git_last_commit: bfb1e34 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GMRP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GMRP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GMRP_1.20.0.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: 22 Package: GNET2 Version: 1.8.0 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: 4570e4a155267f585ec66b05f2c41af0 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_13 git_last_commit: c9db7ec git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GNET2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GNET2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GNET2_1.8.0.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: 92 Package: GOexpress Version: 1.26.0 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) MD5sum: a6348f5c6749a158fda3da8b2d4802ff 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_13 git_last_commit: 9aca228 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOexpress_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOexpress_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOexpress_1.26.0.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: 94 Package: GOfuncR Version: 1.12.0 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, testthat License: GPL (>= 2) MD5sum: 95eec0e61159a74c57ac895a8e7dcf1f 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 (23-Mar-2020). 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_13 git_last_commit: 5a31fd1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOfuncR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOfuncR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOfuncR_1.12.0.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 importsMe: ABAEnrichment dependencyCount: 54 Package: GOpro Version: 1.18.0 Depends: R (>= 3.4) 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 MD5sum: c8fccc76dcc6d66ee2e889f63f841bd7 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_13 git_last_commit: b5688fa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOpro_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOpro_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOpro_1.18.0.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: 1.54.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 Archs: i386, x64 MD5sum: a38e734d545c2853e890a143b59e670c 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_13 git_last_commit: df63e70 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/goProfiles_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goProfiles_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goProfiles_1.54.0.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: 51 Package: GOSemSim Version: 2.18.1 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 License: Artistic-2.0 MD5sum: ca2ad5756076b0ac3ecdf19c6b6b83de 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], 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_13 git_last_commit: d2edfc5 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/GOSemSim_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOSemSim_2.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GOSemSim_2.18.1.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, BioMedR, LANDD suggestsMe: BioCor, epiNEM, FELLA, SemDist, protr, rDNAse dependencyCount: 47 Package: goseq Version: 1.44.0 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: i386, x64 MD5sum: 51c9e92ff758d44d3c4a7e3593537c57 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_13 git_last_commit: 4868dfb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/goseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goseq_1.44.0.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 dependencyCount: 102 Package: GOSim Version: 1.30.0 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) Archs: i386, x64 MD5sum: 6847e670ab712e0f0ae06e0b631b5fb3 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_13 git_last_commit: cd2c2c0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOSim_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOSim_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOSim_1.30.0.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: 65 Package: goSTAG Version: 1.16.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 08863603ff6cda7d0294ce255cb4e167 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_13 git_last_commit: 01e14e9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/goSTAG_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goSTAG_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goSTAG_1.16.0.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: 73 Package: GOstats Version: 2.58.0 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 Archs: i386, x64 MD5sum: 6f577abe48d5ad70c6d5f5b97dfe2d5a 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_13 git_last_commit: d3406a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOstats_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOstats_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOstats_2.58.0.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, pcaExplorer, scTensor, systemPipeR, DNLC, LANDD suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA, maGUI, sand dependencyCount: 63 Package: GOsummaries Version: 2.28.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) MD5sum: e3fec21e898ce1e39dd7c7f726039bad 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_13 git_last_commit: 6b27e45 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOsummaries_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOsummaries_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOsummaries_2.28.0.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: 48 Package: GOTHiC Version: 1.28.0 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: i386, x64 MD5sum: 590c5a065016e1b2aa753f748113b8a0 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_13 git_last_commit: 4898bfc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GOTHiC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOTHiC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOTHiC_1.28.0.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: 81 Package: goTools Version: 1.66.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: f1bc415ec826ec6e2ff6abaacb83b2d3 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_13 git_last_commit: cc7fafe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/goTools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goTools_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goTools_1.66.0.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: 47 Package: GPA Version: 1.4.0 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: ab63acb2e9f7a78a9ad44db233323a5b 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_13 git_last_commit: df84978 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GPA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GPA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GPA_1.4.0.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: 71 Package: gpart Version: 1.10.0 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 Archs: i386, x64 MD5sum: a4447db156abeda91e7ae9c39584aad6 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: RELEASE_3_13 git_last_commit: 39e0086 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gpart_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpart_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpart_1.10.0.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: 107 Package: gpls Version: 1.64.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: ee3a2a5a9504a6a75785d44e56c19d9e 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_13 git_last_commit: f8dc89b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gpls_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpls_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpls_1.64.0.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: 1.36.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 7c90c2ca478bdfe36d8765f9d6c07fd2 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: RELEASE_3_13 git_last_commit: 3db6999 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gprege_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gprege_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gprege_1.36.0.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 dependsOnMe: robin dependencyCount: 45 Package: gpuMagic Version: 1.8.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 3f62c474bf6de46bd8a07c10685573d1 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 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_13 git_last_commit: b7bdd0f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gpuMagic_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpuMagic_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpuMagic_1.8.0.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: 39 Package: granulator Version: 1.0.0 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 Archs: i386, x64 MD5sum: 66ef1163bd9609c466d12240ccad1990 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_13 git_last_commit: 5730b42 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/granulator_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/granulator_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/granulator_1.0.0.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 dependencyCount: 66 Package: graper Version: 1.8.0 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: 0c6173b3274db27c4b68a0d63ea31bdd 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_13 git_last_commit: f94ff44 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/graper_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graper_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graper_1.8.0.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: 43 Package: graph Version: 1.70.0 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 MD5sum: 4b13e3e8fce8fcaf1f7927181590edb1 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_13 git_last_commit: 1c28350 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/graph_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graph_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graph_1.70.0.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, dlsem, geneNetBP, gridGraphviz, GUIProfiler, hasseDiagram, msSurv, NFP, PairViz, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, AnnotationHubData, BgeeDB, BiocCheck, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, DAPAR, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga, flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM, GeneNetworkBuilder, 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, BayesNetBP, BiDAG, BNrich, ceg, CePa, classGraph, CodeDepends, cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase, gridDebug, gRim, HEMDAG, hmma, HydeNet, kpcalg, MetaClean, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, rsolr, SEMgraph, SourceSet, stablespec, topologyGSA, unifDAG, wiseR, zenplots suggestsMe: AnnotationDbi, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct, bsub, ccdrAlgorithm, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebn, sparsebnUtils, textplot, tidygraph dependencyCount: 7 Package: GraphAlignment Version: 1.56.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 865ae515d976408dcab44d3688c11137 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_13 git_last_commit: 3b476a9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GraphAlignment_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphAlignment_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphAlignment_1.56.0.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: 1.64.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 32dae02ecd942edece6dcc76b198c14e 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_13 git_last_commit: cf236c4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GraphAT_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphAT_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphAT_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 23 Package: graphite Version: 1.38.0 Depends: R (>= 2.10), methods Imports: AnnotationDbi, checkmate, graph (>= 1.67.1), httr, rappdirs, stats, utils Suggests: 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: 474d53e4417610fcf159b2fa689c5ba2 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 , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales VignetteBuilder: knitr, R.rsp git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_13 git_last_commit: 7517460 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/graphite_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graphite_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graphite_1.38.0.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, StarBioTrek, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping, NFP, SEMgraph, SourceSet dependencyCount: 50 Package: GraphPAC Version: 1.34.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 25ec2e4272de63e0780588dbae594819 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_13 git_last_commit: 763bd51 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GraphPAC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphPAC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphPAC_1.34.0.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: 1.44.0 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) Archs: i386, x64 MD5sum: b8854e7d5340e3130e817bb69591ea32 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_13 git_last_commit: fa79c31 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GRENITS_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRENITS_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRENITS_1.44.0.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: 45 Package: GreyListChIP Version: 1.24.0 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: i386, x64 MD5sum: 2704de9e3625160e90a4f790c1424c1b 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: Gordon Brown git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_13 git_last_commit: 2a86668 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GreyListChIP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GreyListChIP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GreyListChIP_1.24.0.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: 46 Package: GRmetrics Version: 1.18.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: 3527d7e31cbc65b566b3335416d4f8fa 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_13 git_last_commit: 28eeed1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GRmetrics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRmetrics_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRmetrics_1.18.0.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: 135 Package: groHMM Version: 1.26.0 Depends: R (>= 3.0.2), 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: 94c326b37c3ee2295fa75e1cffa8bbea 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_13 git_last_commit: 6be33d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/groHMM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/groHMM_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/groHMM_1.26.0.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: 45 Package: GRridge Version: 1.16.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 MD5sum: 4908bd4e8e82d58c75e8f1c693609c84 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_13 git_last_commit: 45dab71 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GRridge_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRridge_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRridge_1.16.0.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: 1.20.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) Archs: i386, x64 MD5sum: 4a6afb19ec3b19d7fb0dac3275b2f903 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_13 git_last_commit: 05ff5ee git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSALightning_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSALightning_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSALightning_1.20.0.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: 1.26.0 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: 9bdc740eccaae3abcfbd6451fba0ea57 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_13 git_last_commit: f3ae269 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSAR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSAR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSAR_1.26.0.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: 11 Package: GSCA Version: 2.22.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 7318a5da26bb2f41ca152f84fb66cd6b 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_13 git_last_commit: 24c5884 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSCA_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSCA_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSCA_2.22.0.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: 72 Package: gscreend Version: 1.6.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat License: GPL-3 Archs: i386, x64 MD5sum: 14b755df22c37703e5ce5ea44fe2efdb 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_13 git_last_commit: eaa4f18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gscreend_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gscreend_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gscreend_1.6.0.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: 43 Package: GSEABase Version: 1.54.0 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: 4767797b8fb23c8726cf976ef7694817 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_13 git_last_commit: 5b59f70 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSEABase_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEABase_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEABase_1.54.0.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, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, TFutils, vissE, msigdb, SingscoreAMLMutations, clustermole, immcp, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, TFEA.ChIP, BaseSet dependencyCount: 50 Package: GSEABenchmarkeR Version: 1.12.1 Depends: Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rmarkdown License: Artistic-2.0 MD5sum: ab9e4124b0208a90cba53a3c343a6bb2 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_13 git_last_commit: 06d448c git_last_commit_date: 2021-07-16 Date/Publication: 2021-07-18 source.ver: src/contrib/GSEABenchmarkeR_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEABenchmarkeR_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEABenchmarkeR_1.12.1.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: 125 Package: GSEAlm Version: 1.52.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 512767e6b0906042f4bd6384f07de72f 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_13 git_last_commit: 12cf851 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSEAlm_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEAlm_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEAlm_1.52.0.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: 7 Package: GSEAmining Version: 1.2.0 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: 53edb681143aedb193a2ffe9c6499cae 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_13 git_last_commit: 862eab4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSEAmining_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEAmining_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEAmining_1.2.0.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: 57 Package: gsean Version: 1.12.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, RANKS, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: a05c09a5727e1395c8b16937efcd44a9 NeedsCompilation: no 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_13 git_last_commit: 211c760 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gsean_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gsean_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gsean_1.12.0.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: 117 Package: GSgalgoR Version: 1.2.1 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 MD5sum: 87bf265e0ef4f9fbd8987f2edad6d3eb 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_13 git_last_commit: aa54268 git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-23 source.ver: src/contrib/GSgalgoR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSgalgoR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GSgalgoR_1.2.1.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: 1.26.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: 378920ab35082f0ac6eeb268962995a9 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_13 git_last_commit: f0f3ac7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSReg_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSReg_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSReg_1.26.0.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: 104 Package: GSRI Version: 2.40.0 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 MD5sum: a5210ffc87bdeea31d05f0e470be2d51 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_13 git_last_commit: 4bba992 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GSRI_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSRI_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSRI_2.40.0.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: 65 Package: GSVA Version: 1.40.1 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix, 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: 4c5007a9e17263c377e0019b0d4ba9b3 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: Justin Guinney [aut, cre], Robert Castelo [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb] Maintainer: Justin Guinney 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_13 git_last_commit: 61b842e git_last_commit_date: 2021-06-03 Date/Publication: 2021-06-06 source.ver: src/contrib/GSVA_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSVA_1.40.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GSVA_1.40.1.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, decoupleR, EGSEA, escape, oppar, singleCellTK, TBSignatureProfiler, TNBC.CMS, clustermole, immcp, psSubpathway, scMappR, SIGN, sigQC, SMDIC suggestsMe: MCbiclust dependencyCount: 78 Package: gtrellis Version: 1.24.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: 7fb8b03642b8e75f9beecad9b571747c 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_13 git_last_commit: d92f3f7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gtrellis_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gtrellis_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gtrellis_1.24.0.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: 26 Package: GUIDEseq Version: 1.22.0 Depends: R (>= 3.2.0), 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 Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: a923a95cbf0ccfbb8f92f98159b3a517 NeedsCompilation: no Title: GUIDE-seq analysis pipeline Description: The package implements GUIDE-seq analysis workflow including functions for 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_13 git_last_commit: d6c83e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GUIDEseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GUIDEseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GUIDEseq_1.22.0.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: 138 Package: Guitar Version: 2.8.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 Archs: i386, x64 MD5sum: f92d747d23021cdaf0add7361da4a373 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_13 git_last_commit: c233129 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Guitar_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Guitar_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Guitar_2.8.0.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: 114 Package: Gviz Version: 1.36.2 Depends: R (>= 4.0), 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: a4e111d7b1d0204c66dda6c2114960f4 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_13 git_last_commit: 742ed80 git_last_commit_date: 2021-07-02 Date/Publication: 2021-07-04 source.ver: src/contrib/Gviz_1.36.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/Gviz_1.36.2.zip mac.binary.ver: bin/macosx/contrib/4.1/Gviz_1.36.2.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, ALPS, ASpediaFI, ASpli, CAGEfightR, DMRcate, ELMER, GenomicInteractions, maser, mCSEA, MEAL, methyAnalysis, methylPipe, motifbreakR, PING, primirTSS, proActiv, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, GenomicRanges, gwascat, interactiveDisplay, InterMineR, Pi, pqsfinder, QuasR, RnBeads, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 141 Package: GWAS.BAYES Version: 1.1.0 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 Archs: i386, x64 MD5sum: df57c2cb8bcbc44319609952c9140b6f 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: master git_last_commit: fdf9102 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/GWAS.BAYES_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWAS.BAYES_1.1.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWAS.BAYES_1.1.0.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: 88 Package: gwascat Version: 2.24.0 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 MD5sum: 27e63da1f93065094222b2c0c4510ee6 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_13 git_last_commit: d251baa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gwascat_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gwascat_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gwascat_2.24.0.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: 128 Package: GWASTools Version: 1.38.0 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: 29be9f5c8e240db94dc2c76ef34cbc05 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_13 git_last_commit: a04d6d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GWASTools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWASTools_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWASTools_1.38.0.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: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 68 Package: gwasurvivr Version: 1.10.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: a271468b981564e344a68fa3571f0f4d 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_13 git_last_commit: a325164 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/gwasurvivr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gwasurvivr_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gwasurvivr_1.10.0.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: 125 Package: GWENA Version: 1.2.0 Depends: R (>= 4.0.0) 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: 9f3a7f61c5e0882fa9f8c11662ceca38 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_13 git_last_commit: a8f00b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/GWENA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWENA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWENA_1.2.0.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: 134 Package: h5vc Version: 2.26.1 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 License: GPL (>= 3) Archs: i386, x64 MD5sum: 10553775e3c6a86054c9eb584f701fa0 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_13 git_last_commit: 95d3f8b git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-23 source.ver: src/contrib/h5vc_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/h5vc_2.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/h5vc_2.26.1.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: 1.34.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 5503ebf4ff6b0354c663819db01c2680 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_13 git_last_commit: 1e6972a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hapFabia_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hapFabia_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hapFabia_1.34.0.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: 9 Package: Harman Version: 1.20.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, methods LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE MD5sum: bfa36891d3e30b637e6f848661b79b08 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: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr BugReports: https://github.com/JasonR055/Harman/issues git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_13 git_last_commit: 44013b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Harman_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Harman_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Harman_1.20.0.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 dependencyCount: 5 Package: Harshlight Version: 1.64.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) MD5sum: 0d8a0ddfb1c4224cadda25728494a36e 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_13 git_last_commit: 0da8ef4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Harshlight_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Harshlight_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Harshlight_1.64.0.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: 28 Package: hca Version: 1.0.3 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, BiocFileCache, tools, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown, LoomExperiment, BiocStyle License: MIT + file LICENSE MD5sum: 8b488f1a6acfeb89453ff4a16ecdea16 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, cre], Martin Morgan [aut] () Maintainer: Maya McDaniel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: RELEASE_3_13 git_last_commit: d0d0c9c git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/hca_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/hca_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/hca_1.0.3.tgz vignettes: vignettes/hca/inst/doc/hca_vignette.html vignetteTitles: Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hca/inst/doc/hca_vignette.R dependencyCount: 49 Package: HDF5Array Version: 1.20.0 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 Archs: i386, x64 MD5sum: 92f1968f6d70442bb1cf260b481ace51 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_13 git_last_commit: 8804048 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HDF5Array_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HDF5Array_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HDF5Array_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: compartmap, GenoGAM, MAGAR, TENxBrainData, TENxPBMCData importsMe: biscuiteer, bsseq, clusterExperiment, cytomapper, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, LoomExperiment, methrix, minfi, MOFA2, netSmooth, recountmethylation, scmeth, scry, signatureSearch, spatialHeatmap, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.GRCh38, curatedTCGAData, HCAData, imcdatasets, MethylSeqData, SingleCellMultiModal suggestsMe: beachmat, BiocSklearn, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MultiAssayExperiment, PDATK, QFeatures, scMerge, scran, sesame, SummarizedExperiment, zellkonverter, digitalDLSorteR dependencyCount: 20 Package: HDTD Version: 1.26.0 Depends: R (>= 3.6) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, markdown License: GPL-3 Archs: i386, x64 MD5sum: 0632092ec4dd63bc71ae5c179ac30004 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_13 git_last_commit: 253d71b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HDTD_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HDTD_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HDTD_1.26.0.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: 1.16.0 Depends: R (>= 3.4) 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: i386, x64 MD5sum: 48bfb95f7570b627edd353ba1aca4651 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_13 git_last_commit: c35acce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/heatmaps_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/heatmaps_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/heatmaps_1.16.0.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: 41 Package: Heatplus Version: 3.0.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: add587e3742327c068014382687f482a 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_13 git_last_commit: 0ab2a5a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Heatplus_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Heatplus_3.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Heatplus_3.0.0.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, RAM dependencyCount: 4 Package: HelloRanges Version: 1.18.0 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 Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: b8f75b406cd6bdf9405b1eab79a29992 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_13 git_last_commit: e9067d1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HelloRanges_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HelloRanges_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HelloRanges_1.18.0.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: 99 Package: HELP Version: 1.50.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 8944301d279d34914e6394d3781b20d1 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_13 git_last_commit: 57092fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HELP_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HELP_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HELP_1.50.0.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: 8 Package: HEM Version: 1.64.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: f34ba6e9c2284be5ab0daf3cb1fccfa1 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_13 git_last_commit: 41dddd9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HEM_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HEM_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HEM_1.64.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: Herper Version: 1.2.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA License: GPL-3 MD5sum: 622d0da449cad34c6f6ecb8b95fcfedd 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_13 git_last_commit: 98047e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Herper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Herper_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Herper_1.2.0.tgz vignettes: vignettes/Herper/inst/doc/Herper.html, vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Herper, Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/Herper.R, vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 17 Package: HGC Version: 1.0.3 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: 0d402ce5cf01db013784022e7bf088d2 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_13 git_last_commit: 786b2ce git_last_commit_date: 2021-07-06 Date/Publication: 2021-07-08 source.ver: src/contrib/HGC_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/HGC_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/HGC_1.0.3.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: 54 Package: hiAnnotator Version: 1.26.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: 7eb67247be479e65fa627add1ed94fa1 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_13 git_last_commit: 8c633cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hiAnnotator_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hiAnnotator_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hiAnnotator_1.26.0.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: 81 Package: HIBAG Version: 1.28.0 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 MD5sum: 7055dae0fa379655f9ca92c43d951d3c 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_13 git_last_commit: dead91d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HIBAG_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIBAG_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HIBAG_1.28.0.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: 1.10.0 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 Archs: i386, x64 MD5sum: 88d41e7dfb014fc45b9d2bbfb79aaeac 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_13 git_last_commit: f242bbd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HiCBricks_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiCBricks_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiCBricks_1.10.0.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: 1.14.0 Depends: R (>= 3.4.0), 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 MD5sum: a4a729e161ae53cb79a35268ff373e2c 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_13 git_last_commit: e1b41c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HiCcompare_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiCcompare_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiCcompare_1.14.0.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: 97 Package: HiCDCPlus Version: 1.0.0 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 Archs: i386, x64 MD5sum: b6cc24956b397154a8cd05f2c6be71d2 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_13 git_last_commit: c666131 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HiCDCPlus_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiCDCPlus_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiCDCPlus_1.0.0.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: 156 Package: hierGWAS Version: 1.22.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 2d2b582483f1f6adcc5fcb50666c22ce 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_13 git_last_commit: 9d27218 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hierGWAS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hierGWAS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hierGWAS_1.22.0.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: 17 Package: hierinf Version: 1.10.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: 1da6cd6175f15948f536bdb2ea641629 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_13 git_last_commit: f13c995 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hierinf_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hierinf_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hierinf_1.10.0.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: 17 Package: HilbertCurve Version: 1.22.0 Depends: R (>= 3.1.2), 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 License: MIT + file LICENSE MD5sum: 52c1d07e85d0998def3dd2a244f37271 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_13 git_last_commit: 32424fc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HilbertCurve_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HilbertCurve_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HilbertCurve_1.22.0.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: 28 Package: HilbertVis Version: 1.50.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: e10e5aab114bdcb14805c31373006c4f 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_13 git_last_commit: 62cc9d1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HilbertVis_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HilbertVis_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HilbertVis_1.50.0.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: 1.50.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 2ffc4f15c17b8bb0791f6473ba4c3671 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_13 git_last_commit: d4cb976 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HilbertVisGUI_1.50.0.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: 1.6.0 Depends: R(>= 3.6), 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 Archs: i386, x64 MD5sum: fb726b23b6cd56e595459cf2d0d08433 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 to statistically test whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. 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 4.2.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_13 git_last_commit: f7ac306 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HiLDA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiLDA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiLDA_1.6.0.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: 123 Package: hipathia Version: 2.8.0 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 MD5sum: 040a809dd118c686652ba204273b8b89 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_13 git_last_commit: c46391b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hipathia_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hipathia_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hipathia_2.8.0.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: 1.4.0 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) MD5sum: fe3aa754f869b46c65e011fa19420997 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_13 git_last_commit: c955c6c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HIPPO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIPPO_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HIPPO_1.4.0.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: 82 Package: hiReadsProcessor Version: 1.28.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat License: GPL-3 MD5sum: 5d107f19a09e580584e3f419e9b406ec 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_13 git_last_commit: daa41a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hiReadsProcessor_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/hiReadsProcessor_1.28.0.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: 90 Package: HIREewas Version: 1.10.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: ae4babff694ebbf3498e5b76fd373196 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_13 git_last_commit: b1f38e4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HIREewas_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIREewas_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HIREewas_1.10.0.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: 1.36.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 Archs: i386, x64 MD5sum: bfff7653d843930fcb2760ca3bc73ccc 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_13 git_last_commit: 012dec8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HiTC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiTC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiTC_1.36.0.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: 45 Package: hmdbQuery Version: 1.12.1 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 2bcd9ff989592b72eeb5b5b25cc6c255 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_13 git_last_commit: c26d2ce git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/hmdbQuery_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/hmdbQuery_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/hmdbQuery_1.12.1.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: 9 Package: HMMcopy Version: 1.34.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: e7e90fbc69a2f94307723700d383135f 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 , Sohrab Shah git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_13 git_last_commit: dc240df git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HMMcopy_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HMMcopy_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HMMcopy_1.34.0.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: 2.52.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 348fc0c235b4b5583b29f33c07aefed7 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_13 git_last_commit: 931d0dd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hopach_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hopach_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hopach_2.52.0.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 dependencyCount: 9 Package: HPAanalyze Version: 1.10.0 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: i386, x64 MD5sum: 70f6df55c3948d96b6be4f8ea3c534c2 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_13 git_last_commit: d9bcfdb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HPAanalyze_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HPAanalyze_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HPAanalyze_1.10.0.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: 49 Package: hpar Version: 1.34.0 Depends: R (>= 3.5.0) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 34f5d57208ccf8870f3599f848f35790 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_13 git_last_commit: 8e1b66d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hpar_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hpar_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hpar_1.34.0.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 dependsOnMe: proteomics importsMe: MetaboSignal suggestsMe: HPAStainR, pRoloc, RforProteomics dependencyCount: 1 Package: HPAStainR Version: 1.2.1 Depends: R (>= 4.0.0), dplyr, tidyr Imports: utils, stats, scales, stringr, tibble, shiny, data.table Suggests: knitr, BiocManager, qpdf, hpar, testthat License: Artistic-2.0 MD5sum: f9f1a36e01263f2ca4b746092d8cfa4a 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_13 git_last_commit: 8333874 git_last_commit_date: 2021-06-09 Date/Publication: 2021-06-10 source.ver: src/contrib/HPAStainR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/HPAStainR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/HPAStainR_1.2.1.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: 58 Package: HTqPCR Version: 1.46.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 Archs: i386, x64 MD5sum: febcc62bc288fe591d2665790a00f2a4 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_13 git_last_commit: 3c75acb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HTqPCR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HTqPCR_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HTqPCR_1.46.0.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: 21 Package: HTSeqGenie Version: 4.22.0 Depends: R (>= 3.0.0), 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: 03c881368a14f9ed9e70b2d9f31edb2a 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: RELEASE_3_13 git_last_commit: ddd1eb5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HTSeqGenie_4.22.0.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: 107 Package: HTSFilter Version: 1.32.0 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 Archs: i386, x64 MD5sum: 5583aa43a9f3db45772226c49698a434 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_13 git_last_commit: bd32ecf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HTSFilter_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HTSFilter_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HTSFilter_1.32.0.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: 1.0.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: 037508820ed95d5ea09da5b1a87e8cd6 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_13 git_last_commit: 52515fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HubPub_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HubPub_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HubPub_1.0.0.tgz vignettes: vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/HubPub.R dependencyCount: 82 Package: HumanTranscriptomeCompendium Version: 1.8.3 Depends: R (>= 4.1) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat, rhdf5client, rmarkdown License: Artistic-2.0 MD5sum: 8e4843cd0e69b341e8ddc91427582878 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, Sequencing Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_13 git_last_commit: 860ed80 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/HumanTranscriptomeCompendium_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/HumanTranscriptomeCompendium_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.1/HumanTranscriptomeCompendium_1.8.3.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: 61 Package: hummingbird Version: 1.2.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 0ca3b180c14b58dc4ff214514568fbb6 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_13 git_last_commit: 1d22c8a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hummingbird_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hummingbird_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hummingbird_1.2.0.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: 27 Package: HybridMTest Version: 1.36.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 75e84bbbd94d2eb1cda636c99cf9aab3 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_13 git_last_commit: b80cc7b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/HybridMTest_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HybridMTest_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HybridMTest_1.36.0.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 dependencyCount: 15 Package: hypeR Version: 1.8.0 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 MD5sum: cc6eb563605be1bda780bc30c51da677 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_13 git_last_commit: 23b7217 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hypeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hypeR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hypeR_1.8.0.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: 104 Package: hyperdraw Version: 1.44.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 684e0e29a39b8f08bf93588fe360b338 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_13 git_last_commit: 35eda74 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hyperdraw_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hyperdraw_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hyperdraw_1.44.0.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: 12 Package: hypergraph Version: 1.64.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 0b5351e011a82124cc51d2000588ced0 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_13 git_last_commit: d4e5e2f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/hypergraph_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hypergraph_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hypergraph_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw, RpsiXML dependencyCount: 8 Package: iASeq Version: 1.36.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 Archs: i386, x64 MD5sum: ccbf2e7f910b6d57577762102c35195f 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_13 git_last_commit: e0a6d24 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iASeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iASeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iASeq_1.36.0.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: 1.10.0 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: be1ebca59cdc1d99b9b03dcae819a9c6 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_13 git_last_commit: 1fe32a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iasva_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iasva_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iasva_1.10.0.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: 35 Package: iBBiG Version: 1.36.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: 35b81a1070de201790e0c81938bcaf3e 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_13 git_last_commit: 28747ce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iBBiG_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iBBiG_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iBBiG_1.36.0.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: 55 Package: ibh Version: 1.40.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 54286485aee92b9b9922eb83258f2825 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_13 git_last_commit: e61603c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ibh_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ibh_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ibh_1.40.0.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: 1.32.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: 7a74ef93615efdc889ffb1fc84f0c517 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_13 git_last_commit: 13d8d38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iBMQ_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iBMQ_1.32.0.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: 41 Package: iCARE Version: 1.20.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE MD5sum: f057e817554a972fe95fec5663a58d3e 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_13 git_last_commit: 9549cae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iCARE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCARE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCARE_1.20.0.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: 70 Package: Icens Version: 1.64.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 356a0559ac67b6d36fd022b1299baedb 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_13 git_last_commit: 8bd4876 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Icens_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Icens_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Icens_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.10.0 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: i386, x64 MD5sum: ffbacf3208ea4205878386ad1b3d6a3d 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_13 git_last_commit: b536bec git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/icetea_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/icetea_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/icetea_1.10.0.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: 129 Package: iCheck Version: 1.22.0 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: 7643bbbd5bc32483649e59459f75b7f8 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_13 git_last_commit: 400b77a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iCheck_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCheck_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCheck_1.22.0.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: 178 Package: iChip Version: 1.46.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 1b3cfefd8a527cf206905392d5ff0aff 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_13 git_last_commit: fd29faf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iChip_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iChip_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iChip_1.46.0.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: 1.28.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: fe6307505fe0e1a72663fc5fd3e21c92 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_13 git_last_commit: fdec867 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iClusterPlus_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iClusterPlus_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iClusterPlus_1.28.0.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: 1.12.0 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 MD5sum: 68aadf45941df536792dc3ddb11c129d 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_13 git_last_commit: 6539bcc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iCNV_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCNV_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCNV_1.12.0.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: 90 Package: iCOBRA Version: 1.20.0 Depends: R (>= 4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: a6b442ea692e7f9f773f977ee7d56e12 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_13 git_last_commit: 6f2c176 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iCOBRA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCOBRA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCOBRA_1.20.0.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: 83 Package: ideal Version: 1.16.1 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, 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: 5b2484ffcdb7c05208e1ee20f17041a2 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_13 git_last_commit: e898da4 git_last_commit_date: 2021-10-07 Date/Publication: 2021-10-10 source.ver: src/contrib/ideal_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ideal_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ideal_1.16.1.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: 1.28.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 Archs: i386, x64 MD5sum: 90c320dcee2715a16a8f188623190672 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_13 git_last_commit: d115002 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IdeoViz_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IdeoViz_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IdeoViz_1.28.0.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: 45 Package: idiogram Version: 1.68.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 Archs: i386, x64 MD5sum: da871f853dc5a00dbe0af648d0b14d6a 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_13 git_last_commit: 626777a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/idiogram_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/idiogram_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/idiogram_1.68.0.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: 50 Package: idpr Version: 1.2.0 Depends: R (>= 4.0.0) 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: 5bf6e55b7f9b6b84ff9c626ec1582626 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_13 git_last_commit: 035b726 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/idpr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/idpr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/idpr_1.2.0.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: 58 Package: idr2d Version: 1.6.0 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, 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: a59fd8d731f7787b1640112409a2cfa5 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_13 git_last_commit: df5b4c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/idr2d_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/idr2d_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/idr2d_1.6.0.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: 69 Package: iGC Version: 1.22.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 07525ed63816c79121c5fe4748a91e48 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_13 git_last_commit: ea783bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iGC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iGC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iGC_1.22.0.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: 1.6.0 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: 4409139db240543d55dc6be57094af19 NeedsCompilation: no Title: Differential gene usage in immune repertoires Description: Decoding the properties of immune repertoires is key in understanding the response of adaptive immunity to challenges such as viral infection. One important task in immune repertoire profiling is the detection of biases in Ig gene usage between biological conditions. IgGeneUsage is a computational tool for the analysis of differential gene usage in immune repertoires. It employs Bayesian hierarchical models to fit complex gene usage data from immune repertoire sequencing experiments and quantifies Ig gene usage biases as probabilities. 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_13 git_last_commit: add6692 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IgGeneUsage_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/IgGeneUsage_1.6.0.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: 81 Package: igvR Version: 1.12.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.9.1) Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo, rtracklayer, VariantAnnotation, RColorBrewer Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f5e645df5e1933b5cdfce64a17b5c23f 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_13 git_last_commit: 947368f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/igvR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/igvR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/igvR_1.12.0.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: 107 Package: IHW Version: 1.20.0 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: 18c60d897af0b91c6cb2bf8649e3a889 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_13 git_last_commit: 1d7e10d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IHW_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IHW_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IHW_1.20.0.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, DGEobj.utils suggestsMe: DEWSeq, metagenomeSeq, SummarizedBenchmark, BloodCancerMultiOmics2017, BisRNA dependencyCount: 10 Package: illuminaio Version: 0.34.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: 4fcd941ac1dee531e8af3bc11394bd54 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_13 git_last_commit: a15b557 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/illuminaio_0.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/illuminaio_0.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/illuminaio_0.34.0.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, crlmm, methylumi, minfi, sesame suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.2.0 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: f7d90d39feb7be0c70d42175554229f2 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_13 git_last_commit: b18c823 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ILoReg_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ILoReg_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ILoReg_1.2.0.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: 115 Package: imageHTS Version: 1.42.0 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: f76394014dcbd8dfc8008fcee63eccfa 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: RELEASE_3_13 git_last_commit: f7a9006 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-23 source.ver: src/contrib/imageHTS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/imageHTS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/imageHTS_1.42.0.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: 104 Package: IMAS Version: 1.16.0 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 MD5sum: 79e7041bfa266f9169c53be74d34a880 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_13 git_last_commit: b45a2cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IMAS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMAS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMAS_1.16.0.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: 125 Package: IMMAN Version: 1.12.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: f2f51e3822a2c98a71d44e325a9b2847 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_13 git_last_commit: 9df3185 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IMMAN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMMAN_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMMAN_1.12.0.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: 1.20.0 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 Archs: i386, x64 MD5sum: 7c572570cdeedce36d345ac21f9deccc 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_13 git_last_commit: e938b1b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ImmuneSpaceR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ImmuneSpaceR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ImmuneSpaceR_1.20.0.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: 132 Package: immunoClust Version: 1.24.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: ba66b8863902b3626ee5c634b0d338bb 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_13 git_last_commit: 87ad450 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/immunoClust_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/immunoClust_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/immunoClust_1.24.0.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: 21 Package: immunotation Version: 1.0.1 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: 143d163652177fb8925a8b1d85497ab7 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_13 git_last_commit: 050aa59 git_last_commit_date: 2021-08-16 Date/Publication: 2021-08-17 source.ver: src/contrib/immunotation_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/immunotation_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/immunotation_1.0.1.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: 1.28.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 2cd85b3ca6990fde3054a5d9346fca15 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_13 git_last_commit: f7fdef4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IMPCdata_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMPCdata_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMPCdata_1.28.0.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: 1.66.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: cd268e8df29f5cff5ce3376b4cd63241 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_13 git_last_commit: 5a92999 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/impute_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/impute_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/impute_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, MetaGxOvarian, FAMT, iC10, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, CancerSubtypes, cola, DExMA, doppelgangR, EGAD, fastLiquidAssociation, genefu, genomation, MAGAR, MatrixQCvis, MEAT, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxPancreas, armada, DIscBIO, lilikoi, maGUI, mi4p, Rnmr1D, samr, speaq, specmine, WGCNA suggestsMe: BioNet, graphite, MethPed, MsCoreUtils, QFeatures, RnBeads, scp, TBSignatureProfiler, TCGAutils, DDPNA, DGCA, GeoTcgaData, GSA dependencyCount: 0 Package: INDEED Version: 2.6.0 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: 64eb10795cbf2b72114ca0747a1c82c0 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_13 git_last_commit: c8037cf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/INDEED_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INDEED_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INDEED_2.6.0.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: 86 Package: infercnv Version: 1.8.1 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, 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: i386, x64 MD5sum: a9ad6b34570b8acd1cee190d171e28ab 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_13 git_last_commit: 18e7f52 git_last_commit_date: 2021-08-16 Date/Publication: 2021-08-17 source.ver: src/contrib/infercnv_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/infercnv_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/infercnv_1.8.1.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 dependencyCount: 113 Package: infinityFlow Version: 1.2.0 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: d896c91fbf8680e306fab9d18c559094 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_13 git_last_commit: c7ea4d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/infinityFlow_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/infinityFlow_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/infinityFlow_1.2.0.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: 43 Package: Informeasure Version: 1.2.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, rmarkdown, testthat, SummarizedExperiment License: GPL-3 MD5sum: 41433bc255f783a96c964786df3a0fa0 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_13 git_last_commit: bc8ba3f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Informeasure_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Informeasure_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Informeasure_1.2.0.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: 2.0.0 Depends: R (>= 3.1), methods, Biobase, GenomicRanges, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, preprocessCore, IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel, Biostrings, dplyr, magrittr, plyranges, readr, RSQLite, DBI, purrr, GenomicFeatures, ggplot2, reshape2 Suggests: RUnit, BiocGenerics, BiocManager, rtracklayer, BiocStyle, knitr, markdown, rmarkdown, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: 945c40f540f13491764085d67064c53d NeedsCompilation: no Title: A Bioconductor package for identifying 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: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription, ImmunoOncology 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_13 git_last_commit: d402f5c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/InPAS_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InPAS_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InPAS_2.0.0.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: 135 Package: INPower Version: 1.28.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: b04e1f9e5b8ca7777f019d19c1e93645 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_13 git_last_commit: 5a4a810 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/INPower_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INPower_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INPower_1.28.0.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: 1.22.0 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: 799ade615501588cfd0af4112a45f611 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_13 git_last_commit: b877791 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/INSPEcT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INSPEcT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INSPEcT_1.22.0.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: 140 Package: InTAD Version: 1.12.0 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: 553387b73a7b4172bdf4f761d6794433 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_13 git_last_commit: 487822e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/InTAD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InTAD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InTAD_1.12.0.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: 140 Package: intansv Version: 1.32.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: d65c8329e27646e0f6bda661a8253a4e 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_13 git_last_commit: b773954 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/intansv_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/intansv_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/intansv_1.32.0.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: 153 Package: interacCircos Version: 1.2.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: af6199447a655a5aafba36172477ead6 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_13 git_last_commit: a4bd6d5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/interacCircos_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interacCircos_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interacCircos_1.2.0.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: 1.20.0 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: ef961683238f0ebc39c2bce806b15b88 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_13 git_last_commit: 020b6c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/InteractionSet_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InteractionSet_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InteractionSet_1.20.0.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 importsMe: CAGEfightR, ChIPpeakAnno, HiCcompare, trackViewer suggestsMe: CAGEWorkflow dependencyCount: 27 Package: InteractiveComplexHeatmap Version: 1.0.0 Depends: R (>= 4.0.0), Imports: ComplexHeatmap (>= 2.7.10), grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer 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: 0c2180046c2bddb66c8a479e573fc617 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_13 git_last_commit: 2c4d5af git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/InteractiveComplexHeatmap_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InteractiveComplexHeatmap_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InteractiveComplexHeatmap_1.0.0.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/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, 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/shiny_dev.R suggestsMe: simplifyEnrichment dependencyCount: 97 Package: interactiveDisplay Version: 1.30.0 Depends: R (>= 2.10), 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 MD5sum: 966ad1fbbc4d33fef8bbb59b1dbff395 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: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_13 git_last_commit: 7c9aab4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/interactiveDisplay_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interactiveDisplay_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interactiveDisplay_1.30.0.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: 107 Package: interactiveDisplayBase Version: 1.30.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: df29aa49cbf9458f655569fb96f77918 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: Shawn Balcome [aut, cre], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_13 git_last_commit: 84be4b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/interactiveDisplayBase_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interactiveDisplayBase_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interactiveDisplayBase_1.30.0.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: 42 Package: InterCellar Version: 1.0.0 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 Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, igraph License: MIT + file LICENSE MD5sum: 7b22b782ebb48333a44881ea0f703439 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, the user can define interaction-pairs modules and link them to significant functional terms from Pathways or Gene Ontology. 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_13 git_last_commit: 094f1da git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/InterCellar_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InterCellar_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InterCellar_1.0.0.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: 228 Package: IntEREst Version: 1.16.0 Depends: R (>= 3.4), 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, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 Archs: i386, x64 MD5sum: 49725658571c118ce7ca30b2405fac89 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_13 git_last_commit: 4d75a7c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IntEREst_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IntEREst_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IntEREst_1.16.0.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: 130 Package: InterMineR Version: 1.14.1 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db, org.Hs.eg.db License: LGPL MD5sum: 97052789bf1e59a46470865e3c8333db 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_13 git_last_commit: 1a090a5 git_last_commit_date: 2021-05-27 Date/Publication: 2021-05-27 source.ver: src/contrib/InterMineR_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/InterMineR_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/InterMineR_1.14.1.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: 60 Package: IntramiRExploreR Version: 1.14.1 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: RDAVIDWebService, gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: 0fc2029d8fa83f258ac36fa94d51a7bf 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/sbhattacharya3/IntramiRExploreR/ VignetteBuilder: knitr BugReports: https://github.com/sbhattacharya3/IntramiRExploreR/issues git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_13 git_last_commit: 7289e0f git_last_commit_date: 2021-10-08 Date/Publication: 2021-10-10 source.ver: src/contrib/IntramiRExploreR_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/IntramiRExploreR_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/IntramiRExploreR_1.14.1.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 32 Package: inveRsion Version: 1.40.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) MD5sum: 1beb5f820da06557cb81cedb81108759 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: RELEASE_3_13 git_last_commit: ac7553c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/inveRsion_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/inveRsion_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/inveRsion_1.40.0.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: 85 Package: IONiseR Version: 2.16.0 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: 42760fdc666cd730ef7ee58cfcea79ab 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_13 git_last_commit: e9be4a9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IONiseR_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IONiseR_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IONiseR_2.16.0.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: 85 Package: iPAC Version: 1.36.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 6d20ee4e6a081e7ab7f035556267d8f4 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_13 git_last_commit: c449c77 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iPAC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iPAC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iPAC_1.36.0.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: 30 Package: ipdDb Version: 1.10.0 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: c4ed30095735618a963d074a28308df5 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_13 git_last_commit: 926b8bd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ipdDb_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ipdDb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ipdDb_1.10.0.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: 88 Package: IPO Version: 1.18.0 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 MD5sum: c7a4e8e6c02d081efd802628554071bc 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_13 git_last_commit: f29adc9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IPO_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IPO_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IPO_1.18.0.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: 128 Package: IRanges Version: 2.26.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.29.19) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 43b060681e67be16c09afd9b25774fb9 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_13 git_last_commit: 3195613 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IRanges_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IRanges_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IRanges_2.26.0.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, 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, methyAnalysis, MotifDb, NADfinder, ORFik, OTUbase, pepStat, periodicDNA, plyranges, proBAMr, PSICQUIC, RepViz, rfPred, rGADEM, rGREAT, RJMCMCNucleosomes, RNAmodR, Scale4C, SCOPE, SGSeq, SICtools, Structstrings, TEQC, 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, ballgown, bamsignals, BBCAnalyzer, beadarray, BiocOncoTK, biovizBase, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, breakpointR, BRGenomics, BSgenome, bsseq, BUMHMM, BumpyMatrix, BUSpaRse, CAGEfightR, 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, CNVPanelizer, CNVRanger, CNVrd2, COCOA, coMET, compEpiTools, ComplexHeatmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, dada2, DAMEfinder, dasper, debrowser, DECIPHER, DegNorm, DelayedMatrixStats, deltaCaptureC, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, dpeak, DRIMSeq, easyRNASeq, EDASeq, eisaR, ELMER, EnrichedHeatmap, enrichTF, ensembldb, epidecodeR, epigraHMM, EpiTxDb, epivizr, epivizrData, erma, esATAC, EventPointer, FastqCleaner, fastseg, fcScan, FilterFFPE, FindMyFriends, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, GenomicTuples, genotypeeval, GenVisR, ggbio, girafe, gmapR, gmoviz, GOfuncR, GOpro, GOTHiC, gpart, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, HiCBricks, HiCcompare, HilbertCurve, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, InPAS, INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap, IntEREst, InterMineR, ipdDb, iSEEu, IsoformSwitchAnalyzeR, isomiRs, IVAS, karyoploteR, LOLA, MACPET, MADSEQ, maser, MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, MesKit, metagene, metagene2, metaseqR2, MethCP, methimpute, methInheritSim, MethReg, methrix, methyAnalysis, methylCC, methylInheritance, methylKit, methylPipe, MethylSeekR, methylSig, methylumi, mia, minfi, MinimumDistance, MIRA, missMethyl, MMAPPR2, Modstrings, mosaics, MOSim, motifbreakR, motifmatchr, msa, MsBackendMassbank, MsBackendMgf, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, mumosa, musicatk, MutationalPatterns, NanoStringNCTools, ncRNAtools, normr, nucleoSim, nucleR, oligoClasses, OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, panelcn.mops, pcaExplorer, pdInfoBuilder, PhIPData, Pi, PICS, PING, plethy, podkat, polyester, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, profileplyr, 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, RiboDiPA, RiboProfiling, riboSeqR, ribosomeProfilingQC, RIPAT, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, roar, Rqc, Rsamtools, RSVSim, RTN, rtracklayer, sarks, SCAN.UPC, SCArray, scHOT, segmentSeq, SeqArray, seqCAT, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SimFFPE, SingleMoleculeFootprinting, sitadela, SMITE, snapcount, SNPhood, soGGi, SomaticSignatures, SparseSignatures, Spectra, spicyR, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, systemPipeR, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, TreeSummarizedExperiment, tricycle, tRNA, tRNAdbImport, tRNAscanImport, tscR, TSRchitect, TVTB, tximeta, UMI4Cats, Uniquorn, universalmotif, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, VariantFiltering, VaSP, wavClusteR, wiggleplotr, XCIR, xcms, 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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.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, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, leeBamViews, MethylSeqData, pd.atdschip.tiling, SomaticCancerAlterations, spatialLIBD, systemPipeRdata, ActiveDriverWGS, alakazam, BinQuasi, crispRdesignR, ExomeDepth, geno2proteo, HiCfeat, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, noisyr, oncoPredict, PACVr, RapidoPGS, RTIGER, Signac, simMP, STRMPS, tidygenomics, utr.annotation, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gwascat, GWASTools, HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, MungeSumstats, regionReport, RTCGA, S4Vectors, SigsPack, splatter, TFutils, yeastRNASeq, cancerTiming, fuzzyjoin, gkmSVM, LDheatmap, pagoo, polyRAD, rliger, seqmagick, Seurat, sigminer, valr linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 8 Package: IRISFGM Version: 1.0.0 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 License: GPL-2 MD5sum: 97aded40520a5e991c3310b4366fb44f 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_13 git_last_commit: aacc387 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IRISFGM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IRISFGM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IRISFGM_1.0.0.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: 1.2.1 Depends: R (>= 4.1), magrittr Imports: utils, reactable, htmltools, dplyr, readr, tidyr, purrr, rlang, tibble, BiocParallel, stringr, fs, zip, lubridate, lifecycle, ggplot2, ggrepel, stats, upsetjs, psych, grDevices, data.table, readxl, tools, Rcapture, plotly Suggests: testthat, covr, knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, roxygen2, vegan, withr, extraDistr License: CC BY 4.0 MD5sum: 1c40c7c03af8415956ecc1a48fc730dc 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_13 git_last_commit: ab38e96 git_last_commit_date: 2021-06-08 Date/Publication: 2021-06-10 source.ver: src/contrib/ISAnalytics_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ISAnalytics_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ISAnalytics_1.2.1.tgz vignettes: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.html, vignettes/ISAnalytics/inst/doc/collision_removal.html, vignettes/ISAnalytics/inst/doc/how_to_import_functions.html, vignettes/ISAnalytics/inst/doc/no_rstudio_usage.html vignetteTitles: Working with aggregate functions, Collision removal functionality, How to use import functions, Using ISAnalytics without RStudio support hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.R, vignettes/ISAnalytics/inst/doc/collision_removal.R, vignettes/ISAnalytics/inst/doc/how_to_import_functions.R, vignettes/ISAnalytics/inst/doc/no_rstudio_usage.R dependencyCount: 97 Package: iSEE Version: 2.4.0 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 MD5sum: 2ecd503198449fae34c67cb9878a6481 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_13 git_last_commit: 5c9f140 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iSEE_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSEE_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSEE_2.4.0.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 dependencyCount: 106 Package: iSEEu Version: 1.4.0 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: 50c2dd454d2c7d118557b7ac74a75a51 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_13 git_last_commit: 51da58e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iSEEu_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSEEu_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSEEu_1.4.0.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: 107 Package: iSeq Version: 1.44.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: c64f212bd3f70ffff13abb6aa3362bde 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_13 git_last_commit: adb8311 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iSeq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSeq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSeq_1.44.0.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: 1.38.0 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 Archs: i386, x64 MD5sum: f0a6f3ed9d983a2a43ec65d91212eb9c 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_13 git_last_commit: 444a501 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/isobar_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/isobar_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/isobar_1.38.0.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: 93 Package: IsoCorrectoR Version: 1.10.0 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 MD5sum: 317d21fb7f96b98421fc684d293b9d8a 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_13 git_last_commit: a18f1fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IsoCorrectoR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoR_1.10.0.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: 44 Package: IsoCorrectoRGUI Version: 1.8.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: f971f25882ffd8d1275410f6fa321f37 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_13 git_last_commit: 7e73d7c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IsoCorrectoRGUI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoRGUI_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoRGUI_1.8.0.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: 47 Package: IsoformSwitchAnalyzeR Version: 1.14.1 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: cc99e5e3f3d66e640e8151395901a94f 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_13 git_last_commit: 215dc6f git_last_commit_date: 2021-10-01 Date/Publication: 2021-10-03 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoformSwitchAnalyzeR_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoformSwitchAnalyzeR_1.14.1.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: 160 Package: IsoGeneGUI Version: 2.28.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: 801fe664ce2416425a857328adeb795d 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: RELEASE_3_13 git_last_commit: 3b72011 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IsoGeneGUI_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoGeneGUI_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoGeneGUI_2.28.0.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: 81 Package: ISoLDE Version: 1.20.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) MD5sum: f58d87e22756378bc7f1d4b01b327b1c 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_13 git_last_commit: d936509 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ISoLDE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ISoLDE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ISoLDE_1.20.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.20.0 Depends: R (>= 3.5), DiscriMiner, 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, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 0d43608d670381601c169b985a776343 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_13 git_last_commit: f09067d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/isomiRs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/isomiRs_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/isomiRs_1.20.0.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: 154 Package: ITALICS Version: 2.52.0 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 MD5sum: 65a7dcef71307fa88012532aacde7366 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_13 git_last_commit: bb3b357 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ITALICS_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ITALICS_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ITALICS_2.52.0.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: 60 Package: iterativeBMA Version: 1.50.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) Archs: i386, x64 MD5sum: ec48084033cc956a1d4ec31cc512453f 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_13 git_last_commit: 66815b3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iterativeBMA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterativeBMA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMA_1.50.0.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: 22 Package: iterativeBMAsurv Version: 1.50.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: b65f06f38db46353488199a4ac9f79fd 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_13 git_last_commit: 9637225 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iterativeBMAsurv_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterativeBMAsurv_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMAsurv_1.50.0.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: 1.14.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE Archs: i386, x64 MD5sum: 2a67f3aec0b979d322dfb7154a68835e 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_13 git_last_commit: 8eb13f4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iterClust_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterClust_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterClust_1.14.0.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: 9 Package: iteremoval Version: 1.12.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: 4979f4cee895cb1ccb27c1febb1d20f2 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: RELEASE_3_13 git_last_commit: d955ef4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/iteremoval_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iteremoval_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iteremoval_1.12.0.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: 56 Package: IVAS Version: 2.12.0 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: c1ba8b6bf7a5e6e3a0eb4e22db28c964 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_13 git_last_commit: 72bbdb6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IVAS_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IVAS_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IVAS_2.12.0.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: 123 Package: ivygapSE Version: 1.14.1 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: a23be592156eabf634ab637800ee087e 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_13 git_last_commit: 85da261 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/ivygapSE_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ivygapSE_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ivygapSE_1.14.1.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: 164 Package: IWTomics Version: 1.16.0 Depends: GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 7d0f3f00d74eb61725137d253e22daea 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_13 git_last_commit: 5299f73 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/IWTomics_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IWTomics_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IWTomics_1.16.0.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: 72 Package: karyoploteR Version: 1.18.0 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, 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: a8d9bc14f4e46f2454e9fd8876961ec8 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_13 git_last_commit: 7dcb80b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/karyoploteR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/karyoploteR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/karyoploteR_1.18.0.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 dependencyCount: 144 Package: KBoost Version: 1.0.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 0bcd4000ffc2555f3dd8e2ad7e402a0c 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_13 git_last_commit: de290b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KBoost_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KBoost_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KBoost_1.0.0.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: 2.50.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: cd0aac2b78c816ad5bd70cfd278d4f05 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_13 git_last_commit: 130f4ca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KCsmart_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KCsmart_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KCsmart_2.50.0.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: 19 Package: kebabs Version: 1.26.1 Depends: R (>= 3.2.0), 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: ae6f170462ba5eec6ba572a90b65ec7c 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_13 git_last_commit: 6abd0f9 git_last_commit_date: 2021-06-18 Date/Publication: 2021-06-20 source.ver: src/contrib/kebabs_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/kebabs_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/kebabs_1.26.1.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: FindMyFriends, odseq suggestsMe: apcluster dependencyCount: 30 Package: KEGGgraph Version: 1.52.0 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: a751cf00e2bb53bc3ea81a7d8faf20d4 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_13 git_last_commit: 295f4c0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KEGGgraph_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGgraph_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGgraph_1.52.0.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, NFP, pathfindR suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges, specmine dependencyCount: 14 Package: KEGGlincs Version: 1.18.0 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 MD5sum: 9234aa5486b7e705141dc6d72f8b646e 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_13 git_last_commit: 899d372 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KEGGlincs_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGlincs_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGlincs_1.18.0.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: 61 Package: keggorthology Version: 2.44.0 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: e1fe3231fe411b359dd7fba5ef4901bf 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_13 git_last_commit: 479043c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/keggorthology_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/keggorthology_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/keggorthology_2.44.0.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: 49 Package: KEGGREST Version: 1.32.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr, markdown License: Artistic-2.0 MD5sum: b8ddb57f3e974ff5396ffbb9e8efd1eb 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_13 git_last_commit: 25656cd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KEGGREST_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGREST_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGREST_1.32.0.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, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA, g2f, MetaDBparse, omu, pathfindR suggestsMe: Category, categoryCompare, GenomicRanges, globaltest, iSEEu, MLP, padma, RTopper, CALANGO, maGUI, ptm, scDiffCom, specmine dependencyCount: 28 Package: KinSwingR Version: 1.10.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 75c1bac2c7e45c059ed8a5d9591c8e31 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_13 git_last_commit: d2364b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/KinSwingR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KinSwingR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KinSwingR_1.10.0.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: 34 Package: kissDE Version: 1.12.0 Imports: aod, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 5ae2dc377be3a07b80f3a9c7223b0eb0 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_13 git_last_commit: c2a1f56 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/kissDE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/kissDE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/kissDE_1.12.0.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: 126 Package: KnowSeq Version: 1.6.3 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: cd1288b4e94c1744b8a9ca2b635de44a 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_13 git_last_commit: b744a16 git_last_commit_date: 2021-10-09 Date/Publication: 2021-10-10 source.ver: src/contrib/KnowSeq_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/KnowSeq_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.1/KnowSeq_1.6.3.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: 166 Package: LACE Version: 1.4.0 Depends: R (>= 4.0.0) Imports: data.tree, graphics, grDevices, igraph, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 5bd61071399616a871878e3e5f08529d 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 [ctb] 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_13 git_last_commit: 9293f28 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LACE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LACE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LACE_1.4.0.tgz vignettes: vignettes/LACE/inst/doc/vignette.pdf vignetteTitles: LACE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/vignette.R dependencyCount: 38 Package: lapmix Version: 1.58.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 5dd52a02688127d4797f3b382f2d26fc 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_13 git_last_commit: c1178cd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lapmix_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lapmix_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lapmix_1.58.0.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: 9 Package: LBE Version: 1.60.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 382dde2da359ba566f47587c03c82cbc 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_13 git_last_commit: 7b28f58 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LBE_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LBE_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LBE_1.60.0.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: 1.22.1 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: 11dfd1b6e61eeb373dbc8803230cd2df 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_13 git_last_commit: 6c98ba3 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/ldblock_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ldblock_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ldblock_1.22.1.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: 107 Package: LEA Version: 3.4.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: 7a1e3e8af0aad7c6461e6aff1cc9a784 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. 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 (lfmm and lfmm2). {\tt LEA} also performs imputation of missing genotypes, and computes predictive values of genetic offsets based on new or future environments. The package includes factor methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It implements latent factor mixed models for identifying LEA is mainly based on optimized programs that can scale with the dimension 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_13 git_last_commit: ed416f8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LEA_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LEA_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LEA_3.4.0.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: 1.26.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: db8ac0d22fdc049884d564686ae9cbf7 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_13 git_last_commit: e2e060f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LedPred_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LedPred_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LedPred_1.26.0.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: 1.2.0 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 MD5sum: 7f4d801797414c1058f919bf5991ab0d 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_13 git_last_commit: 6976537 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lefser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lefser_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lefser_1.2.0.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: 66 Package: les Version: 1.42.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 755bee14e02554d838920e079435252a 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_13 git_last_commit: 9224538 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/les_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/les_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/les_1.42.0.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: 1.10.0 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 LinkingTo: Rcpp License: GPL (>= 2) MD5sum: 81d58fa0d1b4e3ae8b602a7653a49ad3 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: Jose Rafael Pilan , Isabelle Mira da Silva , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_13 git_last_commit: 55fd6fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/levi_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/levi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/levi_1.10.0.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: 98 Package: lfa Version: 1.22.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 7e443aa5fcdf75ef29f6b7562381f9c3 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_13 git_last_commit: 4bc83c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lfa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lfa_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lfa_1.22.0.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, jackstraw suggestsMe: popkin dependencyCount: 2 Package: limma Version: 3.48.3 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) MD5sum: dc171a69a1cf4dcd1f9afaa3bc558ccd 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_13 git_last_commit: 80282e9 git_last_commit_date: 2021-08-09 Date/Publication: 2021-08-10 source.ver: src/contrib/limma_3.48.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/limma_3.48.3.zip mac.binary.ver: bin/macosx/contrib/4.1/limma_3.48.3.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, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR2, mpra, 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, CORM, countTransformers, cp4p, DAAGbio, DRomics, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ASpediaFI, ATACseqQC, attract, autonomics, AWFisher, ballgown, BatchQC, beadarray, biotmle, BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, CancerSubtypes, casper, ChAMP, clusterExperiment, CNVRanger, coexnet, combi, compcodeR, consensusDE, consensusOV, CountClust, 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, escape, EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, Glimma, GOsummaries, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MatrixQCvis, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, MIGSA, miloR, minfi, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, 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, SPsimSeq, STATegRa, sva, systemPipeR, timecourse, TimeSeriesExperiment, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, BPM, Cascade, cinaR, DCGL, DGEobj.utils, DiPALM, dsb, GWASbyCluster, immcp, INCATome, lilikoi, lipidomeR, maGUI, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, nlcv, Patterns, plfMA, RANKS, RPPanalyzer, scBio, scRNAtools, SQDA, ssizeRNA, statVisual, tinyarray, wrProteo suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocSet, BioNet, BioQC, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, fgsea, fishpond, gage, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, randRotation, Rcade, recountmethylation, ribosomeProfilingQC, rtracklayer, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, dnet, GeoTcgaData, hexbin, LPS, NACHO, propr, protti, seqgendiff, Seurat, st, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 5 Package: limmaGUI Version: 1.68.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: 68aa2226215f50f0561b2930f11acec9 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_13 git_last_commit: 10cfb5f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/limmaGUI_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/limmaGUI_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/limmaGUI_1.68.0.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: 1.12.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 MD5sum: ef562395cc25d73e646eded8edadeceb 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_13 git_last_commit: 6b86f9a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LineagePulse_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LineagePulse_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LineagePulse_1.12.0.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: 90 Package: LinkHD Version: 1.6.0 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 Archs: i386, x64 MD5sum: 899a0da7bdeffc1cbd4eecee1378368f 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_13 git_last_commit: 6b9017a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LinkHD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LinkHD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LinkHD_1.6.0.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: 129 Package: Linnorm Version: 2.16.0 Depends: R(>= 3.4) 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, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 4dbbd6b977485f16dba867aff643dfd2 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_13 git_last_commit: 5c45f1e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Linnorm_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Linnorm_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Linnorm_2.16.0.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: 69 Package: lionessR Version: 1.6.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE Archs: i386, x64 MD5sum: 0d212fb66eb4f464498648ce15b07229 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_13 git_last_commit: 8d66d42 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lionessR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lionessR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lionessR_1.6.0.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: 26 Package: lipidr Version: 2.6.0 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: a367017c30d1517c43257e03405c9432 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_13 git_last_commit: 23f05f8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lipidr_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lipidr_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lipidr_2.6.0.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 dependencyCount: 90 Package: LiquidAssociation Version: 1.46.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 48973f765092fb230ba0a8367019c4ac 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_13 git_last_commit: 25fba07 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LiquidAssociation_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LiquidAssociation_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LiquidAssociation_1.46.0.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: 67 Package: lisaClust Version: 1.0.0 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: ae57322ad7f6f1564526c86ad45b28ca 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_13 git_last_commit: 1e46ec3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lisaClust_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lisaClust_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lisaClust_1.0.0.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: 93 Package: lmdme Version: 1.34.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: 7bee40f44fbf72aa7e65204b5a2a71ab 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_13 git_last_commit: d60d48d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lmdme_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lmdme_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lmdme_1.34.0.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: 1.18.1 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 24e6a6a5b2516c69064a279f9bc3e034 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: James Collins , Henry Holm , Daniel Lowenstein 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_13 git_last_commit: fec93aa git_last_commit_date: 2021-08-30 Date/Publication: 2021-08-31 source.ver: src/contrib/LOBSTAHS_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/LOBSTAHS_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/LOBSTAHS_1.18.1.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: 126 Package: loci2path Version: 1.12.0 Depends: R (>= 3.4) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: b03605fb5d9ae697a1f03541ed414f82 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_13 git_last_commit: fd21ec3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/loci2path_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/loci2path_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/loci2path_1.12.0.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: 42 Package: logicFS Version: 2.12.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: dc71a515f80972b58923cfc81ee25740 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_13 git_last_commit: fe75c2c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/logicFS_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/logicFS_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/logicFS_2.12.0.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: 1.50.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) MD5sum: 6e5759e3267017d64e00aea28f336202 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_13 git_last_commit: aab4a92 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/logitT_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/logitT_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/logitT_1.50.0.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: 13 Package: LOLA Version: 1.22.0 Depends: R (>= 2.10) 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: b8434ae6ceac7cab4a650d51c1560ef7 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_13 git_last_commit: 43df70e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LOLA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LOLA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LOLA_1.22.0.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: 25 Package: LoomExperiment Version: 1.10.1 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 MD5sum: 2e44f91eb5563b841cf39bfab0fd7faf 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_13 git_last_commit: 347a524 git_last_commit_date: 2021-05-23 Date/Publication: 2021-05-23 source.ver: src/contrib/LoomExperiment_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/LoomExperiment_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/LoomExperiment_1.10.1.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: 36 Package: LowMACA Version: 1.22.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 12d975a93155673c6dd7b71ce1b1e937 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: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_13 git_last_commit: b9b4a18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LowMACA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LowMACA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LowMACA_1.22.0.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: 86 Package: LPE Version: 1.66.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: f568b92837a583382797fcbfbfe90b87 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_13 git_last_commit: a178516 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LPE_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LPE_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LPE_1.66.0.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: 1.52.0 Depends: LPE Imports: LPE, stats License: LGPL Archs: i386, x64 MD5sum: 7528e3f8ac3ca551bd08a5c1e6cbe555 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_13 git_last_commit: 40e7947 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LPEadj_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LPEadj_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LPEadj_1.52.0.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: 2.24.0 Depends: lpSolve License: Artistic License 2.0 MD5sum: 91b78e5825100a7730ec40f5d9f8adeb 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_13 git_last_commit: 3348f9c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lpNet_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lpNet_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lpNet_2.24.0.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: 1.20.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: 7cedbc58491619948f68295bf0839a64 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_13 git_last_commit: 5bb6274 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lpsymphony_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lpsymphony_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lpsymphony_1.20.0.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, TestDesign dependencyCount: 0 Package: LRBaseDbi Version: 2.2.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: a18c4e42401e05e062944f522c6cfd9e 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_13 git_last_commit: 4c1b2fd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LRBaseDbi_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LRBaseDbi_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LRBaseDbi_2.2.0.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 dependsOnMe: LRBase.Ath.eg.db, LRBase.Bta.eg.db, LRBase.Cel.eg.db, LRBase.Dme.eg.db, LRBase.Dre.eg.db, LRBase.Gga.eg.db, LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBase.Pab.eg.db, LRBase.Rno.eg.db, LRBase.Ssc.eg.db, LRBase.Xtr.eg.db suggestsMe: scTensor dependencyCount: 46 Package: LRcell Version: 1.0.0 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 MD5sum: f6bcada9d194b822c40df5734259181f 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_13 git_last_commit: 85370d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LRcell_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LRcell_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LRcell_1.0.0.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: 113 Package: lumi Version: 2.44.0 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: 5d68a531ac7ec09e7fea4af5ff7aad0b 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_13 git_last_commit: 9783629 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/lumi_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lumi_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lumi_2.44.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData importsMe: arrayMvout, ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, beadarrayFilter, maGUI dependencyCount: 159 Package: LymphoSeq Version: 1.20.0 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: 6af4ed60853166e1fa6974d58730a5e8 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_13 git_last_commit: 672762d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/LymphoSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LymphoSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LymphoSeq_1.20.0.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: 1.14.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 Archs: i386, x64 MD5sum: ed459b5f5bf0ba90ffa3a72ad9da84d6 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_13 git_last_commit: 9875790 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/M3C_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/M3C_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/M3C_1.14.0.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: lilikoi suggestsMe: parameters dependencyCount: 62 Package: M3Drop Version: 1.18.0 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: 9646f045cdaf547a7dbbbc1e6cab2f09 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_13 git_last_commit: e393b5a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/M3Drop_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/M3Drop_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/M3Drop_1.18.0.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: 82 Package: maanova Version: 1.62.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) MD5sum: 9acfea7c57464f8f81eecb26be47f9b1 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_13 git_last_commit: 72e45cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maanova_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maanova_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maanova_1.62.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: Maaslin2 Version: 1.6.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, MuMIn, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl Suggests: knitr, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: df4204c6932adc994e8f43875c327af7 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_13 git_last_commit: 9daea4b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Maaslin2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Maaslin2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Maaslin2_1.6.0.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: MMUPHin dependencyCount: 151 Package: macat Version: 1.66.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 76d558b28ab96ebcf510de2188f80071 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_13 git_last_commit: 0b67287 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/macat_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/macat_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/macat_1.66.0.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: 49 Package: maCorrPlot Version: 1.62.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: e87058aad19a4e9e294fa4aaf1d3e709 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_13 git_last_commit: eca52bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maCorrPlot_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maCorrPlot_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maCorrPlot_1.62.0.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: 1.12.0 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: 39ca5cf6b0bde68b14dd1cfb7515c8ff 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: RELEASE_3_13 git_last_commit: 2e34842 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MACPET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MACPET_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MACPET_1.12.0.tgz 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: 103 Package: MACSQuantifyR Version: 1.6.0 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 Archs: i386, x64 MD5sum: 22609fe4e7a8f2804bd8751d50348005 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_13 git_last_commit: 2958ed8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MACSQuantifyR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MACSQuantifyR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MACSQuantifyR_1.6.0.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: 75 Package: MACSr Version: 1.0.0 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: da75aa1e76767e5a2f4f42065ac78993 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_13 git_last_commit: 783b2b1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MACSr_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/MACSr_1.0.0.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: 97 Package: made4 Version: 1.66.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: b6e98bb2cabe2f7f2879d4c131661219 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_13 git_last_commit: d444c18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/made4_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/made4_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/made4_1.66.0.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: 36 Package: MADSEQ Version: 1.18.0 Depends: R(>= 3.4), 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: 984872bcc704fd998cbd77ae07202009 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_13 git_last_commit: 64cfc9c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MADSEQ_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MADSEQ_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MADSEQ_1.18.0.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: 115 Package: maftools Version: 2.8.05 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival 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: f17ee5e9acf7af22ea25b07ac4d69e67 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_13 git_last_commit: 0c14f2a git_last_commit_date: 2021-09-07 Date/Publication: 2021-09-09 source.ver: src/contrib/maftools_2.8.05.tar.gz win.binary.ver: bin/windows/contrib/4.1/maftools_2.8.05.zip mac.binary.ver: bin/macosx/contrib/4.1/maftools_2.8.05.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Somatic status of cancer hotspots, 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/maftools.R, vignettes/maftools/inst/doc/oncoplots.R importsMe: CIMICE, musicatk, TCGAbiolinksGUI, TCGAWorkflow, oncoPredict, pathwayTMB, Rediscover, sigminer, SMDIC suggestsMe: MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 14 Package: MAGAR Version: 1.0.1 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 Archs: i386, x64 MD5sum: a1af2d54c54afdfb40f8230a2d4b50d4 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_13 git_last_commit: 7901096 git_last_commit_date: 2021-07-06 Date/Publication: 2021-07-08 source.ver: src/contrib/MAGAR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAGAR_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MAGAR_1.0.1.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: 194 Package: MAGeCKFlute Version: 1.12.0 Depends: R (>= 3.5) Imports: Biobase, clusterProfiler (>= 3.16.1), enrichplot, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils Suggests: biomaRt, BiocStyle, DOSE, dendextend, graphics, knitr, msigdbr, pheatmap, png, pathview, scales, sva, testthat, License: GPL (>=3) MD5sum: 128ad63923b5677171da652db9ac30f6 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_13 git_last_commit: 119a2de git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MAGeCKFlute_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAGeCKFlute_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAGeCKFlute_1.12.0.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: 126 Package: maigesPack Version: 1.56.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) MD5sum: 72437ecde6546b31e1da8386c2ce5707 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_13 git_last_commit: 3f54ee8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maigesPack_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maigesPack_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maigesPack_1.56.0.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: 13 Package: MAIT Version: 1.26.0 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: 2961ba04377fd69a09fcc46ba2849a22 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_13 git_last_commit: 5d0e003 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MAIT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAIT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAIT_1.26.0.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 suggestsMe: specmine dependencyCount: 207 Package: makecdfenv Version: 1.68.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 114de15f559c9c107789e521b9b6b1d2 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_13 git_last_commit: f277e17 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/makecdfenv_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/makecdfenv_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/makecdfenv_1.68.0.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: 13 Package: MANOR Version: 1.64.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: bed31cae3d4d16c04b3d40278598e89c 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_13 git_last_commit: 6c2e394 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MANOR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MANOR_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MANOR_1.64.0.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: 1.62.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 432d7b1f70b7a99f5687676eab6d00ad 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_13 git_last_commit: f1b59c5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MantelCorr_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MantelCorr_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MantelCorr_1.62.0.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: 1.22.0 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: i386, x64 MD5sum: d9505d305358682b5b538c0e6f2a95c5 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_13 git_last_commit: 3f87c86 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mAPKL_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mAPKL_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mAPKL_1.22.0.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: 82 Package: maPredictDSC Version: 1.30.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 2425de6deb8669ac1cd4acff6c436037 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_13 git_last_commit: 69aac22 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maPredictDSC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maPredictDSC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maPredictDSC_1.30.0.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: 130 Package: mapscape Version: 1.16.0 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 Archs: i386, x64 MD5sum: 21335fd0652b858c5fee3ba5b119e3f9 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_13 git_last_commit: dfb96d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mapscape_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mapscape_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mapscape_1.16.0.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: 1.2.0 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) Archs: i386, x64 MD5sum: d2da18374fbfe5cc462deabd5acd28e2 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_13 git_last_commit: f158650 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/marr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/marr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/marr_1.2.0.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: 61 Package: marray Version: 1.70.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 0c4b48276c25dcb69e2328d24563b30b 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_13 git_last_commit: 9f63605 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/marray_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/marray_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/marray_1.70.0.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, maGUI dependencyCount: 6 Package: martini Version: 1.12.0 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 MD5sum: 0bc0b4388c0629ed0aec99676e219161 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_13 git_last_commit: 70d4c22 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/martini_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/martini_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/martini_1.12.0.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: 1.10.0 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: 2761f54c38eb5150a8706966b73ecec6 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_13 git_last_commit: c37ef16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maser_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maser_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maser_1.10.0.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: 149 Package: maSigPro Version: 1.64.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 4c097da1ab99a3bb70ca02a42864551e 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_13 git_last_commit: 2e4cf8e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maSigPro_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maSigPro_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maSigPro_1.64.0.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: 12 Package: maskBAD Version: 1.36.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) Archs: i386, x64 MD5sum: 0cd6e532bb03e8fcc9bd4ca68730126b 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_13 git_last_commit: b1e1a3a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/maskBAD_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maskBAD_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maskBAD_1.36.0.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: 26 Package: MassArray Version: 1.44.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 666fe7f37dde5593bd9e8031dc40d1fe 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_13 git_last_commit: fdc3f88 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MassArray_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MassArray_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MassArray_1.44.0.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: 1.28.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 941c3d08548db509507cd8749ab1967f 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_13 git_last_commit: 303c8d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/massiR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/massiR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/massiR_1.28.0.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: 15 Package: MassSpecWavelet Version: 1.58.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) MD5sum: b9d7ebdf64eca536f2e2e3130da1dbc4 NeedsCompilation: yes 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, Warren Kibbe, Simon Lin Maintainer: Pan Du git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_13 git_last_commit: cbc5406 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MassSpecWavelet_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MassSpecWavelet_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MassSpecWavelet_1.58.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, Rnmr1D, speaq dependencyCount: 5 Package: MAST Version: 1.18.0 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: b92a9603a42bffe8a959df0ddbd3d7c0 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_13 git_last_commit: 27cc634 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MAST_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAST_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAST_1.18.0.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: celaref, singleCellTK suggestsMe: clusterExperiment, Seurat dependencyCount: 67 Package: matchBox Version: 1.34.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 4f08ea9e1fd836a092f569b2ed09d518 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_13 git_last_commit: 6bd6775 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/matchBox_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/matchBox_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/matchBox_1.34.0.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: 1.4.3 Depends: matrixStats (>= 0.60.1) Imports: methods Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 11515a0894edb3b98967e59324b0bc8f 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_13 git_last_commit: a651a24 git_last_commit_date: 2021-08-25 Date/Publication: 2021-08-26 source.ver: src/contrib/MatrixGenerics_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixGenerics_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixGenerics_1.4.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: CoreGx, MinimumDistance, PDATK, RaggedExperiment, scone, scPCA, tLOH, VanillaICE dependencyCount: 2 Package: MatrixQCvis Version: 1.0.0 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), UpSetR (>= 1.4.0), 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), 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: a1976b174074a36208b6d7bf1f46038f 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_13 git_last_commit: fb7fe47 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MatrixQCvis_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixQCvis_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixQCvis_1.0.0.tgz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: QC for metabolomics and proteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 163 Package: MatrixRider Version: 1.24.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 Archs: i386, x64 MD5sum: e8ef659ce4e65e63616550c97da1a6bf 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_13 git_last_commit: 9eb06fd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MatrixRider_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixRider_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixRider_1.24.0.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: 1.18.0 Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats, biglm Imports: BiocGenerics, ProtGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 026dc5e55870e50ee7981b6d78afccb4 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_13 git_last_commit: dbca756 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/matter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/matter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/matter_1.18.0.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: 22 Package: MBAmethyl Version: 1.26.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 2cdce15b5580569bbd43a16ed401ebb4 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_13 git_last_commit: 69e4737 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MBAmethyl_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBAmethyl_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBAmethyl_1.26.0.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: 1.26.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 862d3395994a1fd882f5d10020a86f95 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_13 git_last_commit: 8b51d18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MBASED_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBASED_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBASED_1.26.0.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: 34 Package: MBCB Version: 1.46.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: a869fa5a2fd02f659af0b38c213196bb 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_13 git_last_commit: 151a5fe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MBCB_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBCB_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBCB_1.46.0.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: mbkmeans Version: 1.8.0 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 Archs: i386, x64 MD5sum: 13b7b96b83cf27ca3edc14b755d4751e 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_13 git_last_commit: 2d2b03a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mbkmeans_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mbkmeans_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mbkmeans_1.8.0.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 importsMe: clusterExperiment, scDblFinder suggestsMe: bluster dependencyCount: 89 Package: mBPCR Version: 1.46.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 490bd8ce140e4d8840f02d330fd6f2b6 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_13 git_last_commit: 9e76c33 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mBPCR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mBPCR_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mBPCR_1.46.0.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: 90 Package: MBQN Version: 2.4.0 Depends: R (>= 4.0) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, rpx, xml2, RCurl, ggplot2, PairedData Suggests: knitr License: GPL-3 + file LICENSE MD5sum: f1ada0a58a3e14b5dde58669f38bc6d2 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: Ariane Schad [aut, cre] (), Clemens Kreutz [aut, ctb] (), Eva Brombacher [aut, ctb] () Maintainer: Ariane Schad 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_13 git_last_commit: 5d61122 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MBQN_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBQN_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBQN_2.4.0.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: 93 Package: MBttest Version: 1.20.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 4960d958318f0d0a3002001a7cfd78b0 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_13 git_last_commit: cd8e6d5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MBttest_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBttest_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBttest_1.20.0.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: 1.16.0 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: ca84d781db8aaec79fddcc2d78dfe340 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_13 git_last_commit: b0ffc3e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MCbiclust_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MCbiclust_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MCbiclust_1.16.0.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: 130 Package: mCSEA Version: 1.12.0 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 Archs: i386, x64 MD5sum: 268a272e886adc3f1f4f01e1c1dc78c4 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_13 git_last_commit: 63a7b6c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mCSEA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mCSEA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mCSEA_1.12.0.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: 154 Package: mdp Version: 1.12.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: b9049a4fb6b6088235222122de78f182 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_13 git_last_commit: 51bbb72 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mdp_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mdp_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mdp_1.12.0.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: 39 Package: mdqc Version: 1.54.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 626d011c14bcf9905c7e370f61ddef88 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_13 git_last_commit: 8301ba4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mdqc_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mdqc_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mdqc_1.54.0.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: 1.12.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: b1c62de68ff87f3605f2c1639dcd9a85 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_13 git_last_commit: e0ad157 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MDTS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MDTS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MDTS_1.12.0.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: 43 Package: MEAL Version: 1.22.0 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: 49b0b71a557ec77f5de559ea8cdadc07 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_13 git_last_commit: bd59164 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEAL_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEAL_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEAL_1.22.0.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: 209 Package: MeasurementError.cor Version: 1.64.0 License: LGPL MD5sum: bca5ce5ddd4e5b8aa467d12044e3fdf8 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_13 git_last_commit: dbc2540 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MeasurementError.cor_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MeasurementError.cor_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MeasurementError.cor_1.64.0.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: 1.4.0 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, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: ea350a0885d97af483623949216dfd73 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_13 git_last_commit: 57d07d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEAT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEAT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEAT_1.4.0.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: 173 Package: MEB Version: 1.6.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: e1f9eaa359701c40f02283d583b376cb 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_13 git_last_commit: 818e7d9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEB_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEB_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEB_1.6.0.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: 30 Package: MEDIPS Version: 1.44.0 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: 767f3c62f5da99910902ca81688e3ef3 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_13 git_last_commit: 4df0aee git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEDIPS_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEDIPS_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEDIPS_1.44.0.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: 102 Package: MEDME Version: 1.52.0 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: 33396ab0832822d1f7a85efcbd4493e1 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_13 git_last_commit: c435a94 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEDME_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEDME_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEDME_1.52.0.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: 112 Package: megadepth Version: 1.2.3 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: 523a3a6347b3f807353e80e4d0322576 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_13 git_last_commit: 844d5f2 git_last_commit_date: 2021-08-23 Date/Publication: 2021-08-24 source.ver: src/contrib/megadepth_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/megadepth_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.1/megadepth_1.2.3.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 dependencyCount: 85 Package: MEIGOR Version: 1.26.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR, knitr License: GPL-3 MD5sum: de02f83bc2ec6db1ca1e6677e76e5c6f 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_13 git_last_commit: c728ace git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MEIGOR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEIGOR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEIGOR_1.26.0.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: 61 Package: Melissa Version: 1.8.0 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: 2564b4540b83c01461a61eaaf073118d 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_13 git_last_commit: 4652c05 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Melissa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Melissa_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Melissa_1.8.0.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: 105 Package: memes Version: 1.0.4 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: dcf034f2fe9e4a252d1a2d612eca8cf2 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_13 git_last_commit: 7a80e64 git_last_commit_date: 2021-08-06 Date/Publication: 2021-08-08 source.ver: src/contrib/memes_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/memes_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.1/memes_1.0.4.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 dependencyCount: 110 Package: Mergeomics Version: 1.20.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: cebef1da0431978871ccbdb2ed422482 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_13 git_last_commit: 30fe263 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Mergeomics_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mergeomics_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mergeomics_1.20.0.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: 1.28.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: 328148f76fbf26facb12b5d2d66226f1 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_13 git_last_commit: 5e396b6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MeSHDbi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MeSHDbi_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MeSHDbi_1.28.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: MeSH.Aca.eg.db, MeSH.Aga.PEST.eg.db, MeSH.Ame.eg.db, MeSH.Aml.eg.db, MeSH.Ana.eg.db, MeSH.Ani.FGSC.eg.db, MeSH.AOR.db, MeSH.Ath.eg.db, MeSH.Bfl.eg.db, MeSH.Bsu.168.eg.db, MeSH.Bta.eg.db, MeSH.Cal.SC5314.eg.db, MeSH.Cbr.eg.db, MeSH.Cel.eg.db, MeSH.Cfa.eg.db, MeSH.Cin.eg.db, MeSH.Cja.eg.db, MeSH.Cpo.eg.db, MeSH.Cre.eg.db, MeSH.Dan.eg.db, MeSH.db, MeSH.Dda.3937.eg.db, MeSH.Ddi.AX4.eg.db, MeSH.Der.eg.db, MeSH.Dgr.eg.db, MeSH.Dme.eg.db, MeSH.Dmo.eg.db, MeSH.Dpe.eg.db, MeSH.Dre.eg.db, MeSH.Dse.eg.db, MeSH.Dsi.eg.db, MeSH.Dvi.eg.db, MeSH.Dya.eg.db, MeSH.Eca.eg.db, MeSH.Eco.K12.MG1655.eg.db, MeSH.Eco.O157.H7.Sakai.eg.db, MeSH.Gga.eg.db, MeSH.Gma.eg.db, MeSH.Hsa.eg.db, MeSH.Laf.eg.db, MeSH.Lma.eg.db, MeSH.Mdo.eg.db, MeSH.Mes.eg.db, MeSH.Mga.eg.db, MeSH.Miy.eg.db, MeSH.Mml.eg.db, MeSH.Mmu.eg.db, MeSH.Mtr.eg.db, MeSH.Nle.eg.db, MeSH.Oan.eg.db, MeSH.Ocu.eg.db, MeSH.Oni.eg.db, MeSH.Osa.eg.db, MeSH.Pab.eg.db, MeSH.Pae.PAO1.eg.db, MeSH.PCR.db, MeSH.Pfa.3D7.eg.db, MeSH.Pto.eg.db, MeSH.Ptr.eg.db, MeSH.Rno.eg.db, MeSH.Sce.S288c.eg.db, MeSH.Sco.A32.eg.db, MeSH.Sil.eg.db, MeSH.Spu.eg.db, MeSH.Ssc.eg.db, MeSH.Syn.eg.db, MeSH.Tbr.9274.eg.db, MeSH.Tgo.ME49.eg.db, MeSH.Tgu.eg.db, MeSH.Vvi.eg.db, MeSH.Xla.eg.db, MeSH.Xtr.eg.db, MeSH.Zma.eg.db importsMe: meshr, scTensor dependencyCount: 46 Package: meshes Version: 1.18.1 Depends: R (>= 3.6.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, MeSH.db, methods, utils, yulab.utils Suggests: knitr, rmarkdown, MeSH.Cel.eg.db, MeSH.Hsa.eg.db, prettydoc License: Artistic-2.0 MD5sum: a1616b978f042cbafb0be0c7eeaedbc6 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] 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_13 git_last_commit: 0a45118 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/meshes_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/meshes_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/meshes_1.18.1.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: 126 Package: meshr Version: 1.28.0 Depends: R (>= 3.0.1) Imports: methods, stats, utils, fdrtool, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db, MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, cummeRbund, org.Hs.eg.db, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 28d5ff00dd6f376ae54828e4805928b1 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 git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_13 git_last_commit: f8913a9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/meshr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/meshr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/meshr_1.28.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 163 Package: MesKit Version: 1.2.0 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: a2548fe44491b861c7713ddec1effeed 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_13 git_last_commit: faa3f2a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MesKit_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MesKit_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MesKit_1.2.0.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: 104 Package: messina Version: 1.28.0 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: d2f9749d47d12daf45ff2821c1bed4a9 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_13 git_last_commit: 16fcd78 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/messina_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/messina_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/messina_1.28.0.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: 44 Package: Metab Version: 1.26.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 962ee3ec889c8f304fc0a7daebdda817 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_13 git_last_commit: dfe078a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Metab_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Metab_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Metab_1.26.0.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: 98 Package: metabCombiner Version: 1.2.2 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 MD5sum: 989f99ab5890848c08c1271e0aa41d05 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_13 git_last_commit: ef53e69 git_last_commit_date: 2021-08-23 Date/Publication: 2021-08-24 source.ver: src/contrib/metabCombiner_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabCombiner_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/metabCombiner_1.2.2.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: MetaboCoreUtils Version: 1.0.0 Depends: R (>= 4.1) Imports: stringr, utils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 6f4166d4d515930185ca9c414317ed05 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] () 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_13 git_last_commit: 0c30b89 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetaboCoreUtils_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaboCoreUtils_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaboCoreUtils_1.0.0.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 dependencyCount: 8 Package: metabolomicsWorkbenchR Version: 1.2.0 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: bfb8249e80c8e5c6642c912726067141 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_13 git_last_commit: 2e1df4c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metabolomicsWorkbenchR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabolomicsWorkbenchR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metabolomicsWorkbenchR_1.2.0.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: 65 Package: metabomxtr Version: 1.26.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 Archs: i386, x64 MD5sum: a69d2b59acade4ea9e53c267032aec20 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_13 git_last_commit: 3cc8a48 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metabomxtr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabomxtr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metabomxtr_1.26.0.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: 56 Package: MetaboSignal Version: 1.22.0 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: 909e504dc4efb1bd629b860e20e1bd61 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_13 git_last_commit: b02adbe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetaboSignal_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaboSignal_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaboSignal_1.22.0.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: 1.20.0 Suggests: knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: c1c4f25051aeed604258b43988991a91 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_13 git_last_commit: d52a6f6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metaCCA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaCCA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaCCA_1.20.0.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: 1.14.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr License: GPL (>= 2) MD5sum: 1c7b84f799a89355c5bfdde0ffed9582 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 git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_13 git_last_commit: 39b62a4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetaCyto_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaCyto_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaCyto_1.14.0.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: 198 Package: metagene Version: 2.24.0 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: 5cf76ef46b8fd7e40f1f9f6e20e494e1 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_13 git_last_commit: f85b3d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metagene_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagene_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metagene_2.24.0.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: 1.8.1 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: e1042fdfbc5fc9327b3167bbe65ac0b0 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_13 git_last_commit: d1121a9 git_last_commit_date: 2021-07-13 Date/Publication: 2021-07-15 source.ver: src/contrib/metagene2_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagene2_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/metagene2_1.8.1.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: 1.34.0 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: 7ee78687f6244d6ab489fec0e4c2c628 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_13 git_last_commit: ff2f710 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metagenomeSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagenomeSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metagenomeSeq_1.34.0.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: Maaslin2, microbiomeDASim, MetaLonDA suggestsMe: interactiveDisplay, phyloseq, Wrench dependencyCount: 28 Package: metahdep Version: 1.50.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: 98e1d287424c8531276b729bfb45b6b2 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_13 git_last_commit: 49da854 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metahdep_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metahdep_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metahdep_1.50.0.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: 1.28.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: fde2f197dba9d1f92de0cc52511cf619 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_13 git_last_commit: 1f3d024 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metaMS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaMS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaMS_1.28.0.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: 126 Package: MetaNeighbor Version: 1.12.0 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 MD5sum: d6b6c4e7fdd050281e1458630154fd2c 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_13 git_last_commit: c11a943 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetaNeighbor_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaNeighbor_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaNeighbor_1.12.0.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: 69 Package: metapod Version: 1.0.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: d6a964e8bf5d602cd50859cf4646d070 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_13 git_last_commit: 704fa80 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metapod_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metapod_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metapod_1.0.0.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: metaSeq Version: 1.32.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 91b699f2c21c4db2c887d39531dbd63a 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_13 git_last_commit: 29543d0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metaSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaSeq_1.32.0.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: 15 Package: metaseqR2 Version: 1.4.0 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) Archs: i386, x64 MD5sum: 87450eca79e00221db44b721c9e9bf26 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_13 git_last_commit: c9bb1dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/metaseqR2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaseqR2_1.4.14.zip mac.binary.ver: bin/macosx/contrib/4.1/metaseqR2_1.4.0.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: 222 Package: metavizr Version: 1.15.0 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, curatedMetagenomicData License: MIT + file LICENSE MD5sum: 8bf146bccf5a2b376b2aad9f2dd656e9 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: master git_last_commit: 24e40f9 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/metavizr_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metavizr_1.15.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metavizr_1.15.0.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: 160 Package: MetaVolcanoR Version: 1.6.0 Depends: R (>= 3.6.0) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, testthat License: GPL-3 MD5sum: 0e43d6a1377d9f2d6378db1e64d062af 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_13 git_last_commit: cc412b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetaVolcanoR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaVolcanoR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaVolcanoR_1.6.0.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: 98 Package: MetCirc Version: 1.22.0 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) Archs: i386, x64 MD5sum: 9fbab0d5dd3985ac2830bfc00106eeb3 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_13 git_last_commit: ff94e6d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetCirc_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetCirc_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetCirc_1.22.0.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: 101 Package: MethCP Version: 1.6.0 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 MD5sum: ed0dbbb306c4a8ca05f3aa9bb9aa08a0 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/MethCP git_branch: RELEASE_3_13 git_last_commit: 4b722d1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethCP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethCP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethCP_1.6.0.tgz vignettes: vignettes/MethCP/inst/doc/methcp.html vignetteTitles: methcp: User’s Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethCP/inst/doc/methcp.R dependencyCount: 108 Package: methimpute Version: 1.14.0 Depends: R (>= 3.4.0), 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 Archs: i386, x64 MD5sum: 92316356a326d492ce4d0681a0845743 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_13 git_last_commit: b24384b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methimpute_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methimpute_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methimpute_1.14.0.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: 59 Package: methInheritSim Version: 1.14.0 Depends: R (>= 3.4) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 07910a57fa73426093efc517b052caed 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_13 git_last_commit: b70d290 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methInheritSim_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methInheritSim_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methInheritSim_1.14.0.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: 1.20.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: 2ea83bb1afe66e604ab6a81eb7136a8f 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_13 git_last_commit: 8b346a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethPed_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethPed_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethPed_1.20.0.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: 9 Package: MethReg Version: 1.2.1 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, 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, sesame, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 MD5sum: e2ef11a1eb3c509519c10e4589727fd3 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_13 git_last_commit: 1a84a50 git_last_commit_date: 2021-05-27 Date/Publication: 2021-05-30 source.ver: src/contrib/MethReg_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethReg_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MethReg_1.2.1.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: 171 Package: methrix Version: 1.6.0 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: fe3845bddb1814ecc3d4775db9a00ad4 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_13 git_last_commit: 74b8250 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methrix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methrix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methrix_1.6.0.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: 1.24.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: e45c0a7f1f301c0c360e38baa3b549ea 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_13 git_last_commit: ca02db6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethTargetedNGS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethTargetedNGS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethTargetedNGS_1.24.0.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: methyAnalysis Version: 1.34.0 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb (>= 1.22.0), GenomicRanges, Biobase (>= 2.34.0), org.Hs.eg.db Imports: grDevices, stats, utils, lumi, methylumi, Gviz, genoset, SummarizedExperiment, IRanges, GenomicRanges, VariantAnnotation, rtracklayer, bigmemoryExtras,GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: ce23e9a79d813f1d58be83edca0a7b64 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A MethyGenoSet class is defined to keep the chromosome location information together with the data. The package also includes functions of estimating the methylation levels from Methy-Seq data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Lei Huang PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/methyAnalysis git_branch: RELEASE_3_13 git_last_commit: 96aa183 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methyAnalysis_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/methyAnalysis_1.34.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi dependencyCount: 192 Package: MethylAid Version: 1.26.0 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: e80b4f434c1c4d8e9b0e469aa2e2a1d6 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_13 git_last_commit: 877d41a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethylAid_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylAid_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylAid_1.26.0.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: 164 Package: methylCC Version: 1.6.0 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: knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: CC BY 4.0 MD5sum: 38b105da59d89428535ed1f1ae9e1240 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_13 git_last_commit: 560219f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylCC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylCC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylCC_1.6.0.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: methylGSA Version: 1.10.0 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: d737ec8552755c04780709a69ac9dc8a 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_13 git_last_commit: 935072a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylGSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylGSA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylGSA_1.10.0.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: 213 Package: methylInheritance Version: 1.16.0 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 MD5sum: a3b00c49542bfbff118446b76dbde5ea 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_13 git_last_commit: 0c4894a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylInheritance_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylInheritance_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylInheritance_1.16.0.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: 1.18.0 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: 18d2329199dd6e51af592930c8ae59dd 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_13 git_last_commit: 8fef778 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylKit_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylKit_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylKit_1.18.0.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: MethCP, methInheritSim, methylInheritance dependencyCount: 94 Package: MethylMix Version: 2.22.0 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: 2db0a87d1ca9b89e683624042a1c8713 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_13 git_last_commit: 612ce15 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethylMix_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylMix_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylMix_2.22.0.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: 53 Package: methylMnM Version: 1.30.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: c27bb054aa2f8e0c1e63be01423c2c63 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_13 git_last_commit: bb6759d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylMnM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylMnM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylMnM_1.30.0.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: 1.26.0 Depends: R (>= 3.2.0), 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: 5752a0296587863bf07777795b0f64e2 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_13 git_last_commit: ca8dade git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylPipe_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylPipe_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylPipe_1.26.0.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: 148 Package: methylscaper Version: 1.0.1 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 MD5sum: 3bea4e2fd7e6e2cda71eaedd50c3c058 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_13 git_last_commit: e9c558f git_last_commit_date: 2021-09-30 Date/Publication: 2021-10-03 source.ver: src/contrib/methylscaper_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylscaper_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/methylscaper_1.0.1.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: 93 Package: MethylSeekR Version: 1.32.0 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: i386, x64 MD5sum: 416e9aca89ff66e0e70b56e49d03414e 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_13 git_last_commit: a68c342 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MethylSeekR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylSeekR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylSeekR_1.32.0.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: 77 Package: methylSig Version: 1.4.0 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: 672d1cde07bfaec6bd7d2257bc14743e 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_13 git_last_commit: a8c68a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylSig_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylSig_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylSig_1.4.0.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: 75 Package: methylumi Version: 2.38.0 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 Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: 414501b1e8fa6c6386723328d5c664c5 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_13 git_last_commit: 13b2e06 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/methylumi_2.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/methylumi_2.38.0.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, methyAnalysis, missMethyl dependencyCount: 153 Package: MetID Version: 1.10.0 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 Archs: i386, x64 MD5sum: 869d4e6f598bae15429654e00e3b2ffa 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_13 git_last_commit: 3378aab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetID_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetID_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetID_1.10.0.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: 116 Package: MetNet Version: 1.10.0 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), Hmisc (>= 4.4-2), GENIE3 (>= 1.7.0), methods (>= 3.5), mpmi (>= 0.42), parmigene (>= 1.0.2), ppcor (>= 1.1), rlang (>= 0.4.10), sna (>= 2.4), stabs (>= 0.6), stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 2.0-18), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1) License: GPL (>= 3) Archs: i386, x64 MD5sum: 8fb8e05b86249e778fcdca72df2b56eb 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_13 git_last_commit: 7e3eee4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MetNet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetNet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetNet_1.10.0.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: 112 Package: mfa Version: 1.14.0 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: 7398a1cfd4495d087309555dc8b24761 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_13 git_last_commit: b2d11d1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mfa_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mfa_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mfa_1.14.0.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: 71 Package: Mfuzz Version: 2.52.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: f77dd8af3a2e27011f3952d3c855b8cf 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_13 git_last_commit: 91496e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Mfuzz_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mfuzz_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mfuzz_2.52.0.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: DAPAR, Patterns suggestsMe: pwOmics dependencyCount: 17 Package: MGFM Version: 1.26.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 27447a5d51d607e8b1daa9011b453120 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_13 git_last_commit: c2c218c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MGFM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MGFM_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MGFM_1.26.0.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: 49 Package: MGFR Version: 1.18.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 MD5sum: 9fa0cdd66b9e9083ef718bf3a40b8e37 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_13 git_last_commit: 9cc8ca1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MGFR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MGFR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MGFR_1.18.0.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: 74 Package: mgsa Version: 1.40.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: 5b12edb853e1765fcc8f516aff935a8b 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_13 git_last_commit: 77f80bf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mgsa_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mgsa_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mgsa_1.40.0.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 dependencyCount: 9 Package: mia Version: 1.0.8 Depends: R (>= 4.1), SummarizedExperiment, SingleCellExperiment, TreeSummarizedExperiment (>= 1.99.3) 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 Archs: i386, x64 MD5sum: 7c9226a22fcceb91ba691c4af036bc4c 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_13 git_last_commit: 95eeac0 git_last_commit_date: 2021-07-30 Date/Publication: 2021-08-01 source.ver: src/contrib/mia_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/mia_1.0.8.zip mac.binary.ver: bin/macosx/contrib/4.1/mia_1.0.8.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 suggestsMe: curatedMetagenomicData dependencyCount: 114 Package: miaViz Version: 1.0.1 Depends: R (>= 4.1), 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: 5ed31ae14843adc8f4eb365ab5c09e6b NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: miaViz 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. biocViews: Microbiome, Software, Visualization Author: Felix G.M. Ernst [aut, cre] (), Tuomas Borman [aut] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: RELEASE_3_13 git_last_commit: 345f172 git_last_commit_date: 2021-06-25 Date/Publication: 2021-06-27 source.ver: src/contrib/miaViz_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/miaViz_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/miaViz_1.0.1.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: 133 Package: MiChip Version: 1.46.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 31ded3a49c080a0e7a76fa62258402c6 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_13 git_last_commit: f25dafd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MiChip_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiChip_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiChip_1.46.0.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: 7 Package: microbiome Version: 1.14.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: 228d381c68d58a8ead919e2dc7b21f16 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_13 git_last_commit: bd8313a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/microbiome_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiome_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiome_1.14.0.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 importsMe: ANCOMBC dependencyCount: 84 Package: microbiomeDASim Version: 1.6.0 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 Archs: i386, x64 MD5sum: a507cf92a6f6af523fd3053aa502d804 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_13 git_last_commit: 880241f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/microbiomeDASim_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiomeDASim_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiomeDASim_1.6.0.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: 1.2.0 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: f516d38629730d783d88adc40751ee84 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_13 git_last_commit: f1b7bd5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/microbiomeExplorer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiomeExplorer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiomeExplorer_1.2.0.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: 205 Package: MicrobiotaProcess Version: 1.4.4 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.3.5), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, picante, plyr, DECIPHER, randomForest, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, corrr, ggupset, ggVennDiagram, gghalves, ggalluvial, forcats, pillar, cli, phyloseq, aplot, ggnewscale, ggside, ggtreeExtra License: GPL (>= 3.0) MD5sum: 0cbf3a641ffc595f5247fead7381a18d NeedsCompilation: no Title: an R package for analysis, visualization and biomarker discovery of microbiome 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 analsys 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_13 git_last_commit: 3305c9c git_last_commit_date: 2021-09-30 Date/Publication: 2021-10-03 source.ver: src/contrib/MicrobiotaProcess_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/MicrobiotaProcess_1.4.4.zip mac.binary.ver: bin/macosx/contrib/4.1/MicrobiotaProcess_1.4.4.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/Introduction.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/Introduction.R dependencyCount: 95 Package: microRNA Version: 1.50.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 199c6fdbd3065917df2645fe70242905 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_13 git_last_commit: a72a3a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/microRNA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microRNA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microRNA_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 19 Package: midasHLA Version: 1.0.0 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: b6bf40d69c1dde3c8afd3292e164ff35 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_13 git_last_commit: 9e6b7ff git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/midasHLA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/midasHLA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/midasHLA_1.0.0.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: 100 Package: MIGSA Version: 1.16.0 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) MD5sum: 16b8ebbdde22ef9b2f3eebdae9882347 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_13 git_last_commit: 021bffb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MIGSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIGSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIGSA_1.16.0.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: 1.0.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, 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, BiocStyle, MouseGastrulationData, magick, RCurl, curl, graphics License: GPL-3 + file LICENSE MD5sum: dcf68be268133fb88791cb95df62eec1 NeedsCompilation: no Title: Differential neighbourhood abundance testing on a graph Description: This package 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_13 git_last_commit: 6e2750a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miloR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miloR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miloR_1.0.0.tgz vignettes: vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html vignetteTitles: 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_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R dependencyCount: 101 Package: mimager Version: 1.16.0 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: eb58c7979fa3e766b7584aa4267aebf0 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_13 git_last_commit: e793400 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mimager_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mimager_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mimager_1.16.0.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: 67 Package: MIMOSA Version: 1.30.0 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 Archs: i386, x64 MD5sum: cfc8965ed26241b73e5e9b37e0d60e0e 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_13 git_last_commit: 8910727 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MIMOSA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIMOSA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIMOSA_1.30.0.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: 91 Package: mina Version: 1.0.0 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: 52eaea105165b8ee48155bf34aa5476e 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_13 git_last_commit: 7bf29d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mina_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mina_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mina_1.0.0.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: 89 Package: MineICA Version: 1.32.0 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 MD5sum: f2b3217ea58b0a057ba6d6b0261dd4fc 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_13 git_last_commit: def84c7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MineICA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MineICA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MineICA_1.32.0.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: 205 Package: minet Version: 3.50.0 Imports: infotheo License: Artistic-2.0 MD5sum: 38515d4651b292fa38e5cd97f305ffec 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_13 git_last_commit: 78c0c53 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/minet_3.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/minet_3.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/minet_3.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, coexnet, epiNEM, RTN, TCGAWorkflow, TGS suggestsMe: CNORfeeder, predictionet, TCGAbiolinks, WGCNA dependencyCount: 1 Package: minfi Version: 1.38.0 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: beebc9cef9252424c977624c4d8b5832 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_13 git_last_commit: ff3bf4a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/minfi_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/minfi_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/minfi_1.38.0.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, DMRcate, 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: EnMCB, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylumi, missMethyl, quantro, recountmethylation, shinyepico, skewr suggestsMe: epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, planet, RnBeads, sesame, brgedata, MLML2R dependencyCount: 138 Package: MinimumDistance Version: 1.36.0 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: i386, x64 MD5sum: 4df5b69e64ce9eeff955d56ff58e87d8 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_13 git_last_commit: 9e0f7b9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MinimumDistance_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MinimumDistance_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MinimumDistance_1.36.0.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: 1.64.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: bd1019ae653ba57f6d098b0263d11c09 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_13 git_last_commit: 765dab9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MiPP_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiPP_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiPP_1.64.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: miQC Version: 1.0.0 Imports: SingleCellExperiment, flexmix, ggplot2, splines, BiocParallel Suggests: scRNAseq, scater, biomaRt, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 440940be524dc67bced4a6d2f971120f 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_13 git_last_commit: d69caf1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/miQC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miQC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miQC_1.0.0.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: 67 Package: MIRA Version: 1.14.0 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 Archs: i386, x64 MD5sum: d8b080725da11febe5f7e2cf0f11f761 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_13 git_last_commit: ac36885 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MIRA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIRA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIRA_1.14.0.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: 1.34.0 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: 4a65a1a6c22b5d6f157d2bb8ec02e635 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_13 git_last_commit: 726e7d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MiRaGE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiRaGE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiRaGE_1.34.0.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: 47 Package: miRBaseConverter Version: 1.16.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 3319b1e9a22ab14ce3e6f1c837b385bf 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_13 git_last_commit: 59eb323 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRBaseConverter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRBaseConverter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRBaseConverter_1.16.0.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: ExpHunterSuite dependencyCount: 1 Package: miRcomp Version: 1.22.0 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: 41a699cb008ca0e6214349041cc4bac2 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_13 git_last_commit: 47b8874 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRcomp_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRcomp_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRcomp_1.22.0.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: 9 Package: mirIntegrator Version: 1.22.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: c0d309ad4809a13ef7ebab3a2163921b 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_13 git_last_commit: 7ce3bbd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mirIntegrator_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mirIntegrator_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mirIntegrator_1.22.0.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: 79 Package: miRLAB Version: 1.22.0 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 License: GPL (>=2) MD5sum: 21dfcfafa26c7da6c014ac528d435348 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: Vu Viet Hoang Pham URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_13 git_last_commit: 829c088 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRLAB_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRLAB_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRLAB_1.22.0.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: 187 Package: miRmine Version: 1.14.0 Depends: R (>= 3.4), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: 0b4195d0ea727fd89e6b25adef35f91f 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_13 git_last_commit: 467a1fd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRmine_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRmine_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRmine_1.14.0.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: 26 Package: miRNAmeConverter Version: 1.20.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 97bb3fde020eca4ba85c30248fcc966f 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_13 git_last_commit: bd0599b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRNAmeConverter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNAmeConverter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNAmeConverter_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 53 Package: miRNApath Version: 1.52.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 23491ea6416c4d732e3a00e34856a3c8 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_13 git_last_commit: 640c48d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRNApath_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNApath_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNApath_1.52.0.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: 1.26.0 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: 947167586a49787cf1103e5b95f4c1b8 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_13 git_last_commit: 22a41a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRNAtap_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNAtap_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNAtap_1.26.0.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: 54 Package: miRSM Version: 1.10.0 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: 31df173c7577afee88bc6e43d4a028f8 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_13 git_last_commit: 7d6b0e5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRSM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRSM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRSM_1.10.0.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: 253 Package: miRspongeR Version: 1.18.0 Depends: R (>= 3.5.0) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, varhandle, linkcomm, utils, Rcpp, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 6abf4d0343deede79ba0f9ab5f515522 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge interaction networks and modules Description: This package provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the 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 modules, and conduct survival analysis of modules. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software 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_13 git_last_commit: d01440c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/miRspongeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRspongeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRspongeR_1.18.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: miRspongeR: identification and analysis of miRNA sponge interaction networks and modules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 141 Package: mirTarRnaSeq Version: 1.0.0 Depends: R (>= 4.1.0) Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils Suggests: BiocStyle, knitr, rmarkdown, R.cache License: MIT MD5sum: e65fcab6f3da1f406e7539b0987a25b1 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_13 git_last_commit: 97072ad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mirTarRnaSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mirTarRnaSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mirTarRnaSeq_1.0.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 50 Package: missMethyl Version: 1.26.1 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 MD5sum: 6eb081a08b381bcaa1058228bc7cfde0 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_13 git_last_commit: 3a43a5e git_last_commit_date: 2021-06-18 Date/Publication: 2021-06-20 source.ver: src/contrib/missMethyl_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/missMethyl_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/missMethyl_1.26.1.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: 163 Package: missRows Version: 1.12.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 7a354fc639d0bb1ef0ff93c44d91a9c4 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_13 git_last_commit: 810c54f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/missRows_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/missRows_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/missRows_1.12.0.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: 66 Package: mistyR Version: 1.0.3 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr, filelock, furrr (>= 0.2.0), ggplot2, MASS, purrr, ranger, readr, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, withr Suggests: BiocStyle, covr, future, igraph, knitr, Matrix, progeny, rmarkdown, sctransform, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 16d0f54d77360e07d755aae686ad1d86 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 Author: Jovan Tanevski [cre, aut] (), Ricardo Omar Ramirez Flores [ctb] () Maintainer: Jovan Tanevski URL: https://github.com/saezlab/mistyR VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: RELEASE_3_13 git_last_commit: cf6b099 git_last_commit_date: 2021-07-22 Date/Publication: 2021-07-22 source.ver: src/contrib/mistyR_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/mistyR_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/mistyR_1.0.3.tgz vignettes: vignettes/mistyR/inst/doc/mistySpatialExperiment.pdf, vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: mistyR and SpatialExperiment, Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R, vignettes/mistyR/inst/doc/mistySpatialExperiment.R dependencyCount: 104 Package: mitch Version: 1.4.1 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 Archs: i386, x64 MD5sum: 77d17638d116ceab6e425047da8734d8 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_13 git_last_commit: 7a8ffff git_last_commit_date: 2021-09-09 Date/Publication: 2021-09-12 source.ver: src/contrib/mitch_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/mitch_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/mitch_1.4.1.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: 97 Package: mixOmics Version: 6.16.3 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: 77b6508053a92a8da802621ce44093a0 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_13 git_last_commit: 759d581 git_last_commit_date: 2021-07-27 Date/Publication: 2021-07-29 source.ver: src/contrib/mixOmics_6.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/mixOmics_6.16.3.zip mac.binary.ver: bin/macosx/contrib/4.1/mixOmics_6.16.3.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, bootsPLS, mixKernel, RGCxGC, sgPLS importsMe: AlpsNMR, DepecheR, multiSight, POMA, MetabolomicsBasics, plsmod, plsRcox, RVAideMemoire suggestsMe: autonomics, ChemoSpec, SelectBoost dependencyCount: 67 Package: MLInterfaces Version: 1.72.0 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 MD5sum: f6d9772bd98587ee4b0be3b30b22cbf9 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_13 git_last_commit: a48dad8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MLInterfaces_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLInterfaces_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLInterfaces_1.72.0.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, proteomics, dGAselID, nlcv dependencyCount: 112 Package: MLP Version: 1.40.0 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, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 8cb92f04e70d8089df592b1849f2db85 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] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_13 git_last_commit: 40d425d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MLP_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLP_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLP_1.40.0.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: 50 Package: MLSeq Version: 2.10.0 Depends: caret, ggplot2 Imports: methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, testthat, BiocStyle, VennDiagram, pamr License: GPL(>=2) MD5sum: 71a990824fa52bb3f6968f621b4defab 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_13 git_last_commit: 2d28c92 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MLSeq_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLSeq_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLSeq_2.10.0.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: 135 Package: MMAPPR2 Version: 1.6.0 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: 4e7811bdab26aef7553eda91de37d931 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_13 git_last_commit: 7c16b5e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MMAPPR2_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/MMAPPR2_1.6.0.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: 104 Package: MMDiff2 Version: 1.20.0 Depends: R (>= 3.3), 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: c2a5883fb9ed8982c4739bfd752dee56 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_13 git_last_commit: d1abce8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MMDiff2_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MMDiff2_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MMDiff2_1.20.0.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: 95 Package: MMUPHin Version: 1.6.2 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: 4fc674258bcd52e0fbafaa893cefb858 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_13 git_last_commit: 5119619 git_last_commit_date: 2021-10-03 Date/Publication: 2021-10-07 source.ver: src/contrib/MMUPHin_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MMUPHin_1.6.2.zip 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: 162 Package: mnem Version: 1.8.0 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: 14081b31cfc48955ab48c3a5f975bb2f 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_13 git_last_commit: b3e3d91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mnem_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mnem_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mnem_1.8.0.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: 84 Package: moanin Version: 1.0.0 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, covr, BiocStyle License: BSD 3-clause License + file LICENSE MD5sum: 0c06cb0e3c8c4328f8f9ed29cf3f7a85 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_13 git_last_commit: 9fbe399 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/moanin_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/moanin_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/moanin_1.0.0.tgz vignettes: vignettes/moanin/inst/doc/documentation.html vignetteTitles: Installation hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/moanin/inst/doc/documentation.R dependencyCount: 96 Package: MODA Version: 1.18.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 9cc83fa44eac1f21a4dfbcc097c62939 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_13 git_last_commit: ee5a413 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MODA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MODA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MODA_1.18.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 111 Package: ModCon Version: 1.0.0 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: a9f2ace3215894fbae49a77298da59b5 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_13 git_last_commit: c25e352 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ModCon_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ModCon_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ModCon_1.0.0.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: 1.8.0 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: 3553b5d321c787750a57543b8b5c5695 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_13 git_last_commit: 8d52df1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Modstrings_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Modstrings_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Modstrings_1.8.0.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: 24 Package: MOFA2 Version: 1.2.2 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 License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: c179e9c976d2633e5f3d8210102df1a0 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_13 git_last_commit: 98a2d44 git_last_commit_date: 2021-08-23 Date/Publication: 2021-08-24 source.ver: src/contrib/MOFA2_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOFA2_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MOFA2_1.2.2.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: 88 Package: MOGAMUN Version: 1.2.1 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: BiocStyle, knitr, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: bca30f71429e10202f229f113c45136e 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_13 git_last_commit: d512ee8 git_last_commit_date: 2021-06-23 Date/Publication: 2021-06-24 source.ver: src/contrib/MOGAMUN_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOGAMUN_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MOGAMUN_1.2.1.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: 73 Package: mogsa Version: 1.26.0 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: 14016f6e6de2f3d6e121a894c9d3fb69 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_13 git_last_commit: 8fbbfc7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mogsa_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mogsa_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mogsa_1.26.0.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: 68 Package: MOMA Version: 1.4.0 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: 42212ca6c0b76e1b79f0f8f35770e432 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_13 git_last_commit: 8a75832 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MOMA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOMA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOMA_1.4.0.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: 96 Package: monocle Version: 2.20.0 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, densityClust (>= 0.3), Rtsne, MASS, reshape2, 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: 17b041f3f6828d3eb8d2ab3c6f58c893 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_13 git_last_commit: dd8f8f2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/monocle_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/monocle_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/monocle_2.20.0.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: tradeSeq, uSORT suggestsMe: M3Drop, scran, sincell, Seurat dependencyCount: 85 Package: MoonlightR Version: 1.18.0 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: 70615486888da4c4d4c7623b0679119e 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*, Catharina Olsen*, Claudia Cava, Thilde Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena Papaleo, Gianluca Bontempi Maintainer: Antonio Colaprico , Catharina Olsen URL: https://github.com/ibsquare/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ibsquare/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_13 git_last_commit: d01eece git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MoonlightR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MoonlightR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MoonlightR_1.18.0.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: 182 Package: mosaics Version: 2.30.0 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) MD5sum: d0c48eccc3bd7614462a2b0638dd08d7 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_13 git_last_commit: d9cceee git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mosaics_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mosaics_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mosaics_2.30.0.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: 41 Package: MOSim Version: 1.6.0 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 MD5sum: 8836e141bd921fd13131f6035581a12f 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_13 git_last_commit: c168670 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MOSim_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOSim_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOSim_1.6.0.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: 57 Package: motifbreakR Version: 2.6.1 Depends: R (>= 3.5.0), grid, MotifDb Imports: methods, compiler, grDevices, grImport, stringr, 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: ca03f49f74c265b34831e0b26f7bbc69 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 22). biocViews: ChIPSeq, Visualization, MotifAnnotation 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_13 git_last_commit: ed220a9 git_last_commit_date: 2021-07-20 Date/Publication: 2021-07-22 source.ver: src/contrib/motifbreakR_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifbreakR_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/motifbreakR_2.6.1.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: 151 Package: motifcounter Version: 1.16.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 Archs: i386, x64 MD5sum: e35bb7bf1ad3585b1d9a25912e1e8821 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_13 git_last_commit: 2e4bd42 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/motifcounter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifcounter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifcounter_1.16.0.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: 19 Package: MotifDb Version: 1.34.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 2fc4a4b61f051f120eb7c70f195764ae 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 git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_13 git_last_commit: ed81ab9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MotifDb_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MotifDb_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MotifDb_1.34.0.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, memes, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 46 Package: motifmatchr Version: 1.14.0 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 Archs: i386, x64 MD5sum: 1742f7d6535e2601f8ab9d1dd72814b2 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_13 git_last_commit: b395fda git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/motifmatchr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifmatchr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifmatchr_1.14.0.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: enrichTF, esATAC, pageRank suggestsMe: chromVAR, MethReg, CAGEWorkflow, Signac dependencyCount: 124 Package: motifStack Version: 1.36.1 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown License: GPL (>= 2) MD5sum: d103c179665482717541ff49704e4759 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_13 git_last_commit: c7c163b git_last_commit_date: 2021-09-30 Date/Publication: 2021-10-03 source.ver: src/contrib/motifStack_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifStack_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.1/motifStack_1.36.1.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, universalmotif dependencyCount: 61 Package: MPFE Version: 1.28.0 License: GPL (>= 3) MD5sum: 552dc29276f2a15ac40eb3887deb0edb 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_13 git_last_commit: 25a44b8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MPFE_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MPFE_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MPFE_1.28.0.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: 1.14.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 9f51d2be6428e63b71befaec9dfd4eef 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_13 git_last_commit: 83bfddd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mpra_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mpra_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mpra_1.14.0.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: 1.10.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: 8357dbadae10b49af6cb4c87773911dc 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_13 git_last_commit: a4175d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MPRAnalyze_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MPRAnalyze_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MPRAnalyze_1.10.0.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: 44 Package: MQmetrics Version: 1.0.0 Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra, utils, stringr, chron, ggpubr, stats, cowplot, RColorBrewer, ggridges, tidyr, scales, grid, rlang, ggforce, grDevices, gtable, plyr, knitr, rmarkdown Suggests: testthat (>= 3.0.0) License: GPL-3 Archs: i386, x64 MD5sum: 4c38bee76d00e91e0e6ca6c984e98905 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 Biognosysin 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_13 git_last_commit: f3e2d25 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MQmetrics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MQmetrics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MQmetrics_1.0.0.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: 118 Package: msa Version: 1.24.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape, phangorn License: GPL (>= 2) MD5sum: 2e9063398d365dd92a77c9eed3b0ed26 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_13 git_last_commit: d939cac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msa_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msa_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msa_1.24.0.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 suggestsMe: idpr, bio3d dependencyCount: 20 Package: MsBackendMassbank Version: 1.0.0 Depends: R (>= 4.0), Spectra (>= 1.0) 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 MD5sum: f3192e26c7b30fc9b7042f01431a566b 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] () 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_13 git_last_commit: 4d24e2c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MsBackendMassbank_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsBackendMassbank_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMassbank_1.0.0.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: 27 Package: MsBackendMgf Version: 1.0.0 Depends: R (>= 4.1), Spectra (>= 1.0) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: b32c1d5f9f126fbca0873ae06a1796de 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_13 git_last_commit: 291d8e9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MsBackendMgf_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsBackendMgf_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMgf_1.0.0.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: xcms dependencyCount: 26 Package: MsCoreUtils Version: 1.4.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, preprocessCore License: Artistic-2.0 MD5sum: 97ceb21933212c6e9add483c694cd88c 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] () 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_13 git_last_commit: edaca2a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MsCoreUtils_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsCoreUtils_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsCoreUtils_1.4.0.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: MsBackendMassbank, MsBackendMgf, MsFeatures, MSnbase, QFeatures, scp, Spectra, xcms suggestsMe: msqrob2 dependencyCount: 13 Package: MSEADbi Version: 1.2.0 Depends: R (>= 4.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: RUnit, BiocGenerics, BiocStyle, knitr, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: c3662ca711bf531c6dc76798f6671d80 NeedsCompilation: no Title: DBI to construct MSEA-related package Description: Interface to construct annotation package for MSEA (MSEA.XXX.pb.db). The program design is same as Bioconductor LRBaseDbi or MeSHDbi pacakge, and the usage is also the same as these packages. biocViews: Infrastructure Author: Kozo Nishida [aut, cre] (), Koki Tsuyuzaki [aut] (), Atsushi Fukushima [aut] () Maintainer: Kozo Nishida VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/MSEADbi git_branch: RELEASE_3_13 git_last_commit: 79634ad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSEADbi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSEADbi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSEADbi_1.2.0.tgz vignettes: vignettes/MSEADbi/inst/doc/MSEADbi.html vignetteTitles: MSEADbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSEADbi/inst/doc/MSEADbi.R dependencyCount: 46 Package: MsFeatures Version: 1.0.0 Depends: R (>= 4.1) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 07b27172c81ecdc841186f9d6bcdb205 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_13 git_last_commit: 7c06b89 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MsFeatures_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsFeatures_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsFeatures_1.0.0.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 dependencyCount: 32 Package: msgbsR Version: 1.16.0 Depends: R (>= 3.4), 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 MD5sum: a3fe1bef56db48fcca23159d9983ffa0 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_13 git_last_commit: eee8300 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msgbsR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msgbsR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msgbsR_1.16.0.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: 165 Package: MSGFgui Version: 1.26.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: ce14fc65dedff041bea3ff2e6aa4aaf9 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFgui git_branch: RELEASE_3_13 git_last_commit: b956389 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSGFgui_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSGFgui_1.26.0.zip vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R dependencyCount: 62 Package: MSGFplus Version: 1.26.0 Depends: methods Imports: mzID, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: d48727c320cfb09393f8ad712b353622 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using the MS-GF+ algorithm. The package contains functionality for building up a parameter set both in code and through a simple GUI, as well as running the algorithm in batches, potentially asynchronously. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFplus git_branch: RELEASE_3_13 git_last_commit: c0c3ae3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSGFplus_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSGFplus_1.26.0.zip vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R dependsOnMe: proteomics importsMe: MSGFgui dependencyCount: 12 Package: msImpute Version: 1.2.0 Depends: R (>= 4.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, rdetools, limma, mvtnorm Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: dcf9a07a1347bae55fdec282158e9a18 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. Currently, msImpute completes missing values by low-rank approximation of the underlying data matrix. 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_13 git_last_commit: 60d2a5e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msImpute_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msImpute_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msImpute_1.2.0.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: 71 Package: msmsEDA Version: 1.30.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: d8c767febcc1c6eb615c4b0a7baa1b09 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_13 git_last_commit: e9ca537 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msmsEDA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msmsEDA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msmsEDA_1.30.0.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: 82 Package: msmsTests Version: 1.30.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 2cb8ad91c9d9cde7e52fd1295be8e573 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_13 git_last_commit: aed00b6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msmsTests_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msmsTests_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msmsTests_1.30.0.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: 90 Package: MSnbase Version: 2.18.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.19.6), S4Vectors, ProtGenerics (>= 1.23.7) 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: cd7e9be5e4fa53657e418c25271d9fd5 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_13 git_last_commit: 8653c08 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSnbase_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSnbase_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSnbase_2.18.0.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: Autotuner, MetCirc, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, xcms, pRolocdata, RforProteomics, proteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, peakPantheR, POMA, PrInCE, ProteomicsAnnotationHubData, ptairMS, topdownr, DAPARdata, qPLEXdata, RAMClustR suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, pmd dependencyCount: 76 Package: MSnID Version: 1.26.0 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: 48e1af3bb36cca32e6a75ee317b025a2 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_13 git_last_commit: 542b32c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSnID_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSnID_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSnID_1.26.0.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 dependsOnMe: proteomics suggestsMe: RforProteomics dependencyCount: 160 Package: MSPrep Version: 1.2.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), VIM, crmn, preprocessCore, sva, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, ddpcr, missForest Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 MD5sum: 190a17cbcd4341cdf19084523aaf384e 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_13 git_last_commit: a135a71 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSPrep_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSPrep_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSPrep_1.2.0.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: 187 Package: msPurity Version: 1.18.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite, uuid, jsonlite Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: 94f0f8b7c583e99e971f20cf35f7de6a 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] 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_13 git_last_commit: d9cf561 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msPurity_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msPurity_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msPurity_1.18.0.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: 75 Package: msqrob2 Version: 1.0.0 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: 120e1d0f66e3561a6640d6e26e672351 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_13 git_last_commit: 7f5bee4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/msqrob2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msqrob2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msqrob2_1.0.0.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: 72 Package: MSstats Version: 4.0.1 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, MSstatsBioData, tinytest, covr, markdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 4a8db1098019f4195d19dd75580c7b66 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_13 git_last_commit: afa9e58 git_last_commit_date: 2021-05-31 Date/Publication: 2021-06-01 source.ver: src/contrib/MSstats_4.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstats_4.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstats_4.0.1.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, MSstatsPTM, MSstatsSampleSize, MSstatsTMT suggestsMe: MSstatsTMTPTM, MSstatsBioData dependencyCount: 63 Package: MSstatsConvert Version: 1.2.2 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7aa2ecb0ad3622a92e6037e6443de6aa 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_13 git_last_commit: bcc4339 git_last_commit_date: 2021-06-15 Date/Publication: 2021-06-17 source.ver: src/contrib/MSstatsConvert_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsConvert_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsConvert_1.2.2.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, MSstatsPTM, MSstatsTMT dependencyCount: 9 Package: MSstatsLOBD Version: 1.0.0 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: 82a7e9ec14f26b7fe9959796595881ab 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_13 git_last_commit: b490f04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSstatsLOBD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsLOBD_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsLOBD_1.0.0.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: 40 Package: MSstatsPTM Version: 1.2.4 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 MD5sum: ffa6cc81d7e3fb35b738167a8d778be3 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_13 git_last_commit: a5c9e20 git_last_commit_date: 2021-10-06 Date/Publication: 2021-10-07 source.ver: src/contrib/MSstatsPTM_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsPTM_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsPTM_1.2.4.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R dependencyCount: 83 Package: MSstatsQC Version: 2.10.0 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: a52fb8e075ac03f3c5fe904d5e81bb73 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_13 git_last_commit: 724c584 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSstatsQC_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsQC_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQC_2.10.0.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: 126 Package: MSstatsQCgui Version: 1.12.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 Archs: i386, x64 MD5sum: e7b201324c1e3fb248ea82c1db4c7774 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_13 git_last_commit: c4b77db git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSstatsQCgui_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsQCgui_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQCgui_1.12.0.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: 128 Package: MSstatsSampleSize Version: 1.6.0 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: ee7aebbb949ecf3c342966a022cd59f4 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_13 git_last_commit: 0e335a0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MSstatsSampleSize_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsSampleSize_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsSampleSize_1.6.0.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: 108 Package: MSstatsTMT Version: 2.0.1 Depends: R (>= 4.0) 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: fed248cc945797df7b7473eefaa061cc 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_13 git_last_commit: 8d487f0 git_last_commit_date: 2021-06-14 Date/Publication: 2021-06-15 source.ver: src/contrib/MSstatsTMT_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsTMT_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsTMT_2.0.1.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, MSstatsTMTPTM dependencyCount: 66 Package: MSstatsTMTPTM Version: 1.1.2 Depends: R (>= 4.0) Imports: dplyr, gridExtra, stringr, reshape2, stats, utils, ggplot2, grDevices, graphics, MSstatsTMT, Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, MSstats, covr License: Artistic-2.0 Archs: i386, x64 MD5sum: 85291fa2ab550bcb46fca3d8387b5d6f NeedsCompilation: yes Title: Post Translational Modification (PTM) Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: Tools for Post Translational Modification (PTM) and protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. The functions in this package should be used after PTM/protein summarization. They can be used to both plot the summarized results and model the summarized datasets. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Tsung-Heng Tsai [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsTMTPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsTMTPTM git_branch: master git_last_commit: d938f9b git_last_commit_date: 2021-02-15 Date/Publication: 2021-03-19 source.ver: src/contrib/MSstatsTMTPTM_1.1.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsTMTPTM_1.1.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsTMTPTM_1.1.2.tgz vignettes: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.html, vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.html vignetteTitles: MSstatsTMTPTM : A package for post translational modification (PTM) significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling", MSstatsTMTPTM.Workflow.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.R, vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.R dependencyCount: 75 Package: Mulcom Version: 1.42.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: 763041df2e7bd75334840c33425b0cc5 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_13 git_last_commit: d21dc82 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Mulcom_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mulcom_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mulcom_1.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 47 Package: MultiAssayExperiment Version: 1.18.0 Depends: R (>= 4.0.0), 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: cf09a85c83a09eec33c7f6bd11937f90 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_13 git_last_commit: b1fa42c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MultiAssayExperiment_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiAssayExperiment_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiAssayExperiment_1.18.0.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, midasHLA, missRows, QFeatures, TimiRGeN, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, CoreGx, corral, ELMER, GOpro, LinkHD, metabolomicsWorkbenchR, MOMA, MultiBaC, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, scp, TCGAutils, HMP2Data suggestsMe: BiocOncoTK, CNVRanger, deco, maftools, MOFA2, MultiDataSet, RaggedExperiment, brgedata, MOFAdata dependencyCount: 46 Package: MultiBaC Version: 1.2.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: 9636ac1e92336cd0e5c1cffc6681ce5d 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_13 git_last_commit: 7f3cc72 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MultiBaC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiBaC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiBaC_1.2.0.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: 1.22.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 28e3f180c1465c3fb8549cf5b56c4632 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_13 git_last_commit: 055e880 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiClust_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiClust_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiClust_1.22.0.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: 47 Package: multicrispr Version: 1.2.0 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 Archs: i386, x64 MD5sum: aa8e1de803e595dadec4ce56d103e89f 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], Johannes Graumann [sad, ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://loosolab.pages.gwdg.de/software/multicrispr/ VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/loosolab/software/multicrispr/-/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: RELEASE_3_13 git_last_commit: 88c1a4f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multicrispr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multicrispr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multicrispr_1.2.0.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: 179 Package: MultiDataSet Version: 1.20.2 Depends: R (>= 3.3), 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 Archs: i386, x64 MD5sum: b9746709458f271e8775016e0d15f1e0 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_13 git_last_commit: 3ef346c git_last_commit_date: 2021-10-07 Date/Publication: 2021-10-10 source.ver: src/contrib/MultiDataSet_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiDataSet_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiDataSet_1.20.2.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: 61 Package: multiGSEA Version: 1.2.0 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: f2de73b3ba4ff7fe26eea3c67fab048e 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_13 git_last_commit: db2c0e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiGSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiGSEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiGSEA_1.2.0.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: 117 Package: multiHiCcompare Version: 1.10.0 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: 3b43dc6a642dbfcbaea2086b23e5b1aa 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_13 git_last_commit: 7780e80 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiHiCcompare_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiHiCcompare_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiHiCcompare_1.10.0.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: 104 Package: MultiMed Version: 2.14.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 52ad7abdfcfe9ef02e928c48c0eed0c4 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_13 git_last_commit: d86037d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MultiMed_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiMed_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiMed_2.14.0.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: 1.14.0 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: d44c483d7a56e6a6f24497b6126ea1e6 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_13 git_last_commit: 2166ba3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiMiR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiMiR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiMiR_1.14.0.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: 58 Package: multiOmicsViz Version: 1.16.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: c1ed278c6ea46a1fb572b15bcc818423 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_13 git_last_commit: 30ea852 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiOmicsViz_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiOmicsViz_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiOmicsViz_1.16.0.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: 1.52.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 8ad053d5e4758334aef82fa5bcd55a62 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_13 git_last_commit: c76196c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiscan_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiscan_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiscan_1.52.0.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: 7 Package: multiSight Version: 1.0.0 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 Suggests: org.Mm.eg.db, rlang, markdown, attempt, processx, testthat, knitr, BiocStyle License: CeCILL + file LICENSE MD5sum: 903c0a71470ee6108097a70ec4722543 NeedsCompilation: no Title: Multi-omics Classification, Functional Enrichment and Network Inference analysis Description: multiSight is an R package providing an user-friendly graphical interface 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. 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_13 git_last_commit: c94fb29 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multiSight_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiSight_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiSight_1.0.0.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: 275 Package: multtest Version: 2.48.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: d4f80c2a5f634474baabf6a2949d0cee 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_13 git_last_commit: 5da1a87 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/multtest_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multtest_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multtest_2.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, GExMap, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, RTopper, SingleCellSignalR, singleCellTK, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, ecolitk, factDesign, GOstats, GSEAlm, maigesPack, ropls, topGO, xcms, cherry, metagam, POSTm dependencyCount: 15 Package: mumosa Version: 1.0.0 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: f0634894f977edc4c6c25ad0d805244f 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_13 git_last_commit: bd0c58c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mumosa_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mumosa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mumosa_1.0.0.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: 1.0.1 Depends: R(>= 4.0) Imports: data.table, utils, stats, GenomicRanges, BSgenome, Biostrings Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, methods, BiocGenerics, IRanges, GenomeInfoDb, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr License: Artistic-2.0 MD5sum: a2193d5bce710d51f1b37564c4f408b4 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 removes duplicates across SNPs. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [cre] (), 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_13 git_last_commit: c1fe775 git_last_commit_date: 2021-06-22 Date/Publication: 2021-06-24 source.ver: src/contrib/MungeSumstats_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MungeSumstats_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MungeSumstats_1.0.1.tgz vignettes: vignettes/MungeSumstats/inst/doc/MungeSumstats.html vignetteTitles: Standardise the format of summary statistics from GWAS with MungeSumstats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/MungeSumstats.R dependencyCount: 46 Package: muscat Version: 1.6.0 Depends: R (>= 4.1) 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, testthat, UpSetR License: GPL (>= 2) Archs: i386, x64 MD5sum: 5aa2fde7deab2108da8be2fd68309f5c 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_13 git_last_commit: d05998b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/muscat_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/muscat_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/muscat_1.6.0.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: 170 Package: muscle Version: 3.34.0 Depends: Biostrings License: Unlimited MD5sum: 80e33f3375e463548c9b454bc8fae64a 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_13 git_last_commit: 49f305f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/muscle_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/muscle_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/muscle_3.34.0.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: ptm suggestsMe: seqmagick dependencyCount: 19 Package: musicatk Version: 1.2.0 Depends: R (>= 4.0.0), NMF Imports: SummarizedExperiment, VariantAnnotation, cowplot, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, maftools, 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, stringi, philentropy Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr License: LGPL-3 Archs: i386, x64 MD5sum: 116f9420b6fa593e8a181eea3fed7514 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_13 git_last_commit: c207280 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/musicatk_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/musicatk_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/musicatk_1.2.0.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: 233 Package: MutationalPatterns Version: 3.2.0 Depends: R (>= 4.1.0), 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) 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 MD5sum: 1e3d6f53804bdad25800261c6dce7548 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 [cre] (), Ruben van Boxtel [aut, cph] (), Edwin Cuppen [aut] () Maintainer: Rurika Oka URL: https://doi.org/10.1186/s13073-018-0539-0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_13 git_last_commit: 80fd57a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MutationalPatterns_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MutationalPatterns_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MutationalPatterns_3.2.0.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: 1.66.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 49943f12166513c85b75710b932d967e 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_13 git_last_commit: 895bfdc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MVCClass_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MVCClass_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MVCClass_1.66.0.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: 1.16.0 Depends: R(>= 3.4) 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: 98658cd7a0800ee2191f830049a86127 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_13 git_last_commit: 4c4de42 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/MWASTools_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MWASTools_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MWASTools_1.16.0.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: 143 Package: mygene Version: 1.28.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 77e918071eb64f7c39be68263114e16b 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_13 git_last_commit: 876884a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mygene_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mygene_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mygene_1.28.0.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: 138 Package: myvariant Version: 1.22.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 9207d50915788a45250de445cef1eff8 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_13 git_last_commit: 926053d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/myvariant_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/myvariant_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/myvariant_1.22.0.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: 136 Package: mzID Version: 1.30.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: a0320028ef68c5af16fb96445d1691ed 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_13 git_last_commit: 455f98b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/mzID_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mzID_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mzID_1.30.0.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 dependsOnMe: proteomics importsMe: MSGFgui, MSGFplus, MSnbase, MSnID suggestsMe: mzR, RforProteomics dependencyCount: 11 Package: mzR Version: 2.26.1 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: 12beea3e36daffe05ec37c3ee9ee4073 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 wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for 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 , Laurent Gatto , Qiakng Kou 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_13 git_last_commit: 5a5c15c5 git_last_commit_date: 2021-06-19 Date/Publication: 2021-06-20 source.ver: src/contrib/mzR_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/mzR_2.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/mzR_2.26.1.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: MSGFgui, MSnbase, proteomics importsMe: adductomicsR, Autotuner, CluMSID, DIAlignR, MSnID, msPurity, peakPantheR, ProteomicsAnnotationHubData, SIMAT, topdownr, xcms, yamss, IDSL.IPA suggestsMe: AnnotationHub, qcmetrics, Spectra, msdata, RforProteomics, erah dependencyCount: 12 Package: NADfinder Version: 1.16.0 Depends: R (>= 3.4), 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: 3beed7e9c063dcc93a00d30a4cf5e08f 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_13 git_last_commit: e592a6b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NADfinder_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NADfinder_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NADfinder_1.16.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 218 Package: NanoMethViz Version: 1.2.0 Depends: R (>= 4.0.0), methods, ggplot2 Imports: cpp11 (>= 0.2.5), readr, S4Vectors, SummarizedExperiment, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, data.table, e1071, fs, GenomicRanges, ggthemes, glue, patchwork, purrr, rlang, RSQLite, Rsamtools, scales, 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) MD5sum: 107e640b3875c91a43a6d9e8d0d0fd08 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_13 git_last_commit: 15c307c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NanoMethViz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoMethViz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoMethViz_1.2.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/ImportingData.html, vignettes/NanoMethViz/inst/doc/Introduction.html vignetteTitles: Importing Data, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/ImportingData.R, vignettes/NanoMethViz/inst/doc/Introduction.R dependencyCount: 133 Package: NanoStringDiff Version: 1.22.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: 5cf7ce325dc2e10bbf34eb625ba3d72c 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_13 git_last_commit: 82f8b18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NanoStringDiff_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringDiff_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringDiff_1.22.0.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 dependencyCount: 9 Package: NanoStringNCTools Version: 1.0.0 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: Artistic-2.0 MD5sum: 2c0626ed05ee3b61291276aeb7bea2ee 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_13 git_last_commit: 962adc0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NanoStringNCTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringNCTools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringNCTools_1.0.0.tgz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools dependencyCount: 71 Package: NanoStringQCPro Version: 1.24.0 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: i386, x64 MD5sum: d47a7b24fad4a1c19f80408bcf1c29e5 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_13 git_last_commit: 1366855 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NanoStringQCPro_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringQCPro_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringQCPro_1.24.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 95 Package: nanotatoR Version: 1.8.0 Depends: R (>= 3.6) Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, grDevices, graphics, stringr, knitr, testthat, utils, AnnotationDbi, httr, org.Hs.eg.db, rtracklayer Suggests: rmarkdown, yaml License: file LICENSE MD5sum: 82bdaae75c9d0bfff6291cea48da4ad7 NeedsCompilation: no Title: nanotatoR: 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 and, more recently, small copy number variants. However, due to utilization of short reads, it is not well suited for identification of structural variants (SV) and the majority of SV calling tools having high false positive and negative rates.Optical next-generation mapping (NGM) utilizes long fluorescently labeled native-state DNA molecules for de novo genome assembly to overcome the limitations of WGS. NGM allows for a significant increase in SV detection capability. However, accuracy of SV annotation is highly important for variant classification and filtration to determine pathogenicity.Here we create a new tool in R, for SV annotation, including population frequency and gene function and expression, using publicly available datasets. We use DGV (Database of Genomic Variants), to calculate the population frequency of the SVs identified by the Bionano SVCaller in the NGM dataset of a cohort of 50 undiagnosed patients with a variety of phenotypes. The new annotation tool, nanotatoR, also calculates the internal frequency of SVs, which could be beneficial in identification of potential false positive or common calls. The software creates a primary gene list (PG) from NCBI databases based on patient phenotype and compares the list to the set of genes overlapping the patient’s SVs generated by SVCaller, providing analysts with an easy way to identify variants affecting genes in the PG. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type. Users then have a choice to filter SVs using the provided annotation for identification of inherited, de novo or rare variants. nanotatoR provides integrated annotation and the expression patterns to enable users to identify potential pathogenic SVs with greater precision and faster turnaround times. 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_13 git_last_commit: 40f3c9e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nanotatoR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nanotatoR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nanotatoR_1.8.0.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: 103 Package: NBAMSeq Version: 1.8.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 Archs: i386, x64 MD5sum: c66dbd7bc5bca07067fa69ecb97523bf 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_13 git_last_commit: c51aa12 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NBAMSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NBAMSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NBAMSeq_1.8.0.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: 1.10.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=2) Archs: i386, x64 MD5sum: e02f8cb256c0dd8f893d9c5a10686238 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_13 git_last_commit: 58b6cb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NBSplice_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NBSplice_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NBSplice_1.10.0.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: 108 Package: ncdfFlow Version: 2.38.0 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: Artistic-2.0 MD5sum: 9a6b591efc4d5df04656994ae72dfcb8 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 , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_13 git_last_commit: 7902e69 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ncdfFlow_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncdfFlow_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ncdfFlow_2.38.0.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: 20 Package: ncGTW Version: 1.6.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: 39728241bdf679ccc5e57e82b4a2e85c 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_13 git_last_commit: cf40068 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ncGTW_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncGTW_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ncGTW_1.6.0.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: 94 Package: NCIgraph Version: 1.40.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 05f222b4f722893b010a56a860cf15d0 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_13 git_last_commit: b75cc38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NCIgraph_1.40.0.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: 69 Package: ncRNAtools Version: 1.2.1 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: f15e076360be9c81db4513afd862ced5 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_13 git_last_commit: cd5ad29 git_last_commit_date: 2021-06-27 Date/Publication: 2021-06-29 source.ver: src/contrib/ncRNAtools_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncRNAtools_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ncRNAtools_1.2.1.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: 59 Package: ndexr Version: 1.14.1 Depends: igraph Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD MD5sum: e83b7eac09b2ea5a6828048e735169c7 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_13 git_last_commit: 57dc82e git_last_commit_date: 2021-07-27 Date/Publication: 2021-07-27 source.ver: src/contrib/ndexr_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ndexr_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ndexr_1.14.1.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R importsMe: netgsa dependencyCount: 39 Package: nearBynding Version: 1.2.0 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 License: Artistic-2.0 MD5sum: 5b49c799169ba4ae164e4c714773a6ae 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 (>= v2.20), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: RELEASE_3_13 git_last_commit: cb38368 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nearBynding_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nearBynding_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nearBynding_1.2.0.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: 123 Package: Nebulosa Version: 1.2.0 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 Archs: i386, x64 MD5sum: df1aa001de5b9b5c140dd94ca77fa703 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_13 git_last_commit: d253c3c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Nebulosa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Nebulosa_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Nebulosa_1.2.0.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 dependencyCount: 165 Package: NeighborNet Version: 1.10.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 Archs: i386, x64 MD5sum: 2cbe1ad13043aa93c05e71e0427f0706 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_13 git_last_commit: 9ca22cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NeighborNet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NeighborNet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NeighborNet_1.10.0.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: 8 Package: nempi Version: 1.0.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown License: GPL-3 MD5sum: 490b7a8b77514556f2315133bbd13a74 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_13 git_last_commit: 86f9104 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nempi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nempi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nempi_1.0.0.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: 1.26.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 5fbc6f872d76ca9df0001e9b1f52bf10 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_13 git_last_commit: 41822fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netbiov_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netbiov_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netbiov_1.26.0.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: 11 Package: netboost Version: 2.0.0 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, rmarkdown License: GPL-3 OS_type: unix MD5sum: 9a55d628d66cb919e831613c2b73abc6 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_13 git_last_commit: daccaa3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netboost_2.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/netboost_2.0.0.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: 113 Package: netboxr Version: 1.4.0 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: a8f6d785cfc8baaac48e2f8b59698752 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] Maintainer: Eirc Minwei Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netboxr git_branch: RELEASE_3_13 git_last_commit: 8b61aeb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netboxr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netboxr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netboxr_1.4.0.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: 139 Package: netDx Version: 1.4.3 Depends: R (>= 3.6) Imports: ROCR,pracma,ggplot2,RCy3,glmnet,igraph,reshape2,parallel,stats,utils,MultiAssayExperiment,graphics,grDevices,methods,BiocFileCache,GenomicRanges,bigmemory,doParallel,foreach,combinat,rappdirs,GenomeInfoDb,S4Vectors,IRanges,RColorBrewer, scater, netSmooth, clusterExperiment,Rtsne,httr Suggests: curatedTCGAData, TCGAutils, rmarkdown, testthat, knitr, BiocStyle License: MIT + file LICENSE MD5sum: e59b857f6f8b34b2bf7440dae57c8f3d 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], 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_13 git_last_commit: 4f053f2 git_last_commit_date: 2021-08-18 Date/Publication: 2021-08-19 source.ver: src/contrib/netDx_1.4.3.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/netDx_1.4.3.tgz vignettes: vignettes/netDx/inst/doc/BuildPredictor.html, vignettes/netDx/inst/doc/ThreeWayClassifier.html, vignettes/netDx/inst/doc/ValidateNew.html vignetteTitles: 01. Build binary predictor and view performance,, top features and integrated Patient Similarity Network, 02. Build three-way classifier (N-way; N>2) from multi-omic data, 04. Validate model with selected features on an independent dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netDx/inst/doc/BuildPredictor.R, vignettes/netDx/inst/doc/ThreeWayClassifier.R, vignettes/netDx/inst/doc/ValidateNew.R dependencyCount: 200 Package: nethet Version: 1.24.0 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: f412a65991835eb5317733faa07b79e5 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_13 git_last_commit: 18df2f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nethet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nethet_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nethet_1.24.0.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: 76 Package: NetPathMiner Version: 1.28.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: f761f7131a3cae1e81661db91c619aac 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_13 git_last_commit: a0182f7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NetPathMiner_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NetPathMiner_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NetPathMiner_1.28.0.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: 11 Package: netprioR Version: 1.18.0 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: 3ec9aa855ae9c857a604e68606e42da3 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_13 git_last_commit: a54190f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netprioR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netprioR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netprioR_1.18.0.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: 52 Package: netresponse Version: 1.52.0 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) MD5sum: 0904d4e993349ecb60c955f1bbe17ac6 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_13 git_last_commit: 1ecd688 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netresponse_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netresponse_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netresponse_1.52.0.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: 56 Package: NetSAM Version: 1.32.0 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: 988570c4be4b1a74c06d1e639451bcd7 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_13 git_last_commit: f04aab5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NetSAM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NetSAM_1.31.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NetSAM_1.32.0.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: 131 Package: netSmooth Version: 1.12.0 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 Archs: i386, x64 MD5sum: 881e6a90b66059f9349afc0d544c3191 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_13 git_last_commit: a9a910f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/netSmooth_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netSmooth_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netSmooth_1.12.0.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 importsMe: netDx dependencyCount: 166 Package: networkBMA Version: 2.32.0 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) Archs: i386, x64 MD5sum: 45586773dea2ffd4bea066444605b042 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: RELEASE_3_13 git_last_commit: ed81202 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/networkBMA_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/networkBMA_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/networkBMA_2.32.0.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: NewWave Version: 1.2.0 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: dbb57a947ee35a639fcb64eca15d23b7 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_13 git_last_commit: 43d1601 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NewWave_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NewWave_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NewWave_1.2.0.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: 41 Package: ngsReports Version: 1.8.1 Depends: R (>= 4.0.0), BiocGenerics, ggplot2, 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 Archs: i386, x64 MD5sum: acafec1041462ec57d4b9db4a22b5c7b 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_13 git_last_commit: 03a044d git_last_commit_date: 2021-06-14 Date/Publication: 2021-06-15 source.ver: src/contrib/ngsReports_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ngsReports_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ngsReports_1.8.1.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: 104 Package: nnNorm Version: 2.56.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 3cb9b83f65d37f7efe6fc667bdb61c1b 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_13 git_last_commit: 04838e4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nnNorm_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nnNorm_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nnNorm_2.56.0.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: NOISeq Version: 2.36.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 3ae8bd1557ccae72b5d191432a1cc8f4 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_13 git_last_commit: c6b5b95 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NOISeq_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NOISeq_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NOISeq_2.36.0.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: CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR dependencyCount: 12 Package: nondetects Version: 2.22.0 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 Archs: i386, x64 MD5sum: b1956e8a3b22b481bc9fcb01e69dc9d9 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_13 git_last_commit: c5d66a7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nondetects_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nondetects_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nondetects_2.22.0.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: 38 Package: NoRCE Version: 1.4.0 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, License: MIT + file LICENSE MD5sum: b07977581a6126fe92188375444e1967 NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) have been found to play critical regulatory roles in biological processes, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs set needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together, and this spatial proximity hints at a functional association. Based on this idea, we present an R package, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out by using the functional annotations of the coding genes located proximally to the input ncRNAs. NoRCE allows incorporating other biological information such as the topologically associating domain (TAD) regions, co-expression patterns, and miRNA target information. NoRCE repository includes several data files, such as cell line specific TAD regions, functional gene sets, and cancer expression data. Additionally, users can input custom data files. Results can be retrieved in a tabular format or viewed as graphs. 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_13 git_last_commit: 9cb604c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NoRCE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NoRCE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NoRCE_1.4.0.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: 123 Package: normalize450K Version: 1.20.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: d30aa9f580e5d4882fef887ba69dadb3 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_13 git_last_commit: 58b8a2c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/normalize450K_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/normalize450K_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/normalize450K_1.20.0.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: 13 Package: NormalyzerDE Version: 1.10.0 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: 86155e63d6599b6bdf949635765c4aeb 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_13 git_last_commit: 94eb4bc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NormalyzerDE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NormalyzerDE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NormalyzerDE_1.10.0.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: 148 Package: NormqPCR Version: 1.38.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: d7f4c619221c55677729e0fbd5030717 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_13 git_last_commit: e4655ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NormqPCR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NormqPCR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NormqPCR_1.38.0.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: 39 Package: normr Version: 1.18.1 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: 75c3e1fab71e24d0e616688e11daeba0 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_13 git_last_commit: f1d3f26 git_last_commit_date: 2021-09-20 Date/Publication: 2021-09-21 source.ver: src/contrib/normr_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/normr_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/normr_1.18.1.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: 1.4.0 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: 1251c81906d35fd5e27b976279d6b915 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_13 git_last_commit: 12d4ac2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NPARC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NPARC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NPARC_1.4.0.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: 39 Package: npGSEA Version: 1.28.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: ffc04cb096f2b707552eee439ec1fc36 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_13 git_last_commit: 7a10483 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/npGSEA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/npGSEA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/npGSEA_1.28.0.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: 51 Package: NTW Version: 1.42.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 23f308495dfec2ef214b0e6bec7687de 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_13 git_last_commit: 9529f61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NTW_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NTW_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NTW_1.42.0.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: 1.20.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: af8c4691d5ae27929c27c8a92b8732f3 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 user has choice between three different distributions for the read positioning: Normal, Student and Uniform. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes 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_13 git_last_commit: c1bfc38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nucleoSim_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nucleoSim_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nucleoSim_1.20.0.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: 9 Package: nucleR Version: 2.24.0 Depends: 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) Archs: i386, x64 MD5sum: 29b5cf44d2ca4dce33769126292bad12 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_13 git_last_commit: b7e30e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nucleR_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nucleR_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nucleR_2.24.0.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: 76 Package: nuCpos Version: 1.10.0 Depends: R (>= 3.6) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: file LICENSE MD5sum: a72a0d1b3f5388b1035671f7cba282f6 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_13 git_last_commit: 64a4e71 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/nuCpos_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nuCpos_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nuCpos_1.10.0.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: NuPoP Version: 2.0.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 68f53196074dd446359a7747b2fc3fd4 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_13 git_last_commit: 57cc4b6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/NuPoP_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NuPoP_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NuPoP_2.0.0.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: occugene Version: 1.52.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 63e44f79bc6add1108561b0aa33c9416 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_13 git_last_commit: 9b6ea7f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/occugene_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/occugene_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/occugene_1.52.0.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: 1.66.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL MD5sum: a38495ffd1e4794bf97e42836c568c6b 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_13 git_last_commit: 9beeb38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OCplus_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OCplus_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OCplus_1.66.0.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: 18 Package: odseq Version: 1.20.0 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: f5a3291473a0fe15ef9c8a0089042877 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_13 git_last_commit: a873deb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/odseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/odseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/odseq_1.20.0.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: 33 Package: oligo Version: 1.56.0 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) Archs: i386, x64 MD5sum: 369448b2fc10bad63fa63e145c4a7100 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_13 git_last_commit: b3b6d7e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oligo_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oligo_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oligo_1.56.0.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: 53 Package: oligoClasses Version: 1.54.0 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) Archs: i386, x64 MD5sum: bb1bdbf80c8122a35ddd3ef1bc837ddd 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_13 git_last_commit: 1b919e6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oligoClasses_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oligoClasses_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oligoClasses_1.54.0.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: 49 Package: OLIN Version: 1.70.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: b1737ab8a410af8d8c5a25a0d269df71 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_13 git_last_commit: 2be671b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OLIN_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OLIN_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OLIN_1.70.0.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: 1.66.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: d1b9fc8bd3e40f0b4b28841c8489b57d 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_13 git_last_commit: 62bd0d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OLINgui_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OLINgui_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OLINgui_1.66.0.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: 2.8.0 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: 3dcad35db8aace83e86a341a26c4b157 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_13 git_last_commit: 73c1e16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OmaDB_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmaDB_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmaDB_2.8.0.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: 1.32.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 393a77bf40a5e589c5e508c3c88c4e52 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_13 git_last_commit: d2d06c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/omicade4_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicade4_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicade4_1.32.0.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: 37 Package: OmicCircos Version: 1.30.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 87844527cfdccc109532c8060f2b8d74 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_13 git_last_commit: 656dd6a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OmicCircos_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmicCircos_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmicCircos_1.30.0.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: 17 Package: omicplotR Version: 1.12.0 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: 633357be9087fdcaca7953b7b555cb36 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_13 git_last_commit: b2013f0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/omicplotR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicplotR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicplotR_1.12.0.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: 97 Package: omicRexposome Version: 1.14.0 Depends: R (>= 3.4), 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: d187d9f9ec02094559eff5f464ae9671 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_13 git_last_commit: f490520 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/omicRexposome_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicRexposome_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicRexposome_1.14.0.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: 212 Package: OmicsLonDA Version: 1.8.0 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: e1e5654ade862908e3ae6175bbf41691 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_13 git_last_commit: edcb4b5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OmicsLonDA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmicsLonDA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmicsLonDA_1.8.0.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: 68 Package: OMICsPCA Version: 1.10.0 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: 3173cded111f916cd1596a4960e34d2f 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_13 git_last_commit: d08002d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OMICsPCA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OMICsPCA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OMICsPCA_1.10.0.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: 218 Package: omicsPrint Version: 1.12.0 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: 60374ce79589f751533c9019287bf872 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_13 git_last_commit: ad214c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/omicsPrint_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicsPrint_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicsPrint_1.12.0.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: 49 Package: Omixer Version: 1.2.4 Depends: R (>= 4.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr, RColorBrewer Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: c1d38d8db6ba47125ab77c355c8b7cae NeedsCompilation: no Title: Randomize Samples for -omics Profiling Description: Omixer - an R package for multivariate and reproducible randomization with lab-friendly sample layouts. Omixer ensures optimal sample distribution across batches with well-documented methods, and can output lab-friendly sample sheets for the wet lab if needed. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: git_url: https://git.bioconductor.org/packages/Omixer git_branch: RELEASE_3_13 git_last_commit: 341f989 git_last_commit_date: 2021-10-06 Date/Publication: 2021-10-07 source.ver: src/contrib/Omixer_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/Omixer_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.1/Omixer_1.2.4.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: 57 Package: OmnipathR Version: 3.0.4 Depends: R(>= 4.0) Imports: checkmate, curl, digest, dplyr, httr, igraph, jsonlite, later, logger, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, stats, stringr, tibble, tidyr, tidyselect, tools, utils, xml2, yaml Suggests: BiocStyle, dnet, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, rmarkdown, smoof, testthat License: MIT + file LICENSE MD5sum: 4e5b472c1b825ff6f63afbaad32bcb03 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_13 git_last_commit: 91e2894 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/OmnipathR_3.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmnipathR_3.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmnipathR_3.0.4.tgz vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html, vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/nichenet.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html vignetteTitles: OmniPath Bioconductor workshop, Building networks around drug-targets using OmnipathR, Using NicheNet with OmnipathR, OmnipathR: an R client for the OmniPath web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R, vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/nichenet.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R importsMe: wppi dependencyCount: 61 Package: oncomix Version: 1.14.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: e2b85ab88d3da2b37a7ffa4f9bc2a794 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_13 git_last_commit: 4c21cd9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oncomix_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oncomix_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oncomix_1.14.0.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: 59 Package: OncoScore Version: 1.20.0 Depends: R (>= 4.0.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: cd82348e51d24682b418a704b035f455 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_13 git_last_commit: f0fc479 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OncoScore_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OncoScore_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OncoScore_1.20.0.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: 72 Package: OncoSimulR Version: 3.0.0 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: 5148f6f48f32dbd7cc00dc1337c38590 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_13 git_last_commit: 3add541 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OncoSimulR_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OncoSimulR_3.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OncoSimulR_3.0.0.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: 103 Package: oneSENSE Version: 1.14.0 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: a918644b877af9447843f70277880dc5 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_13 git_last_commit: 732f387 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oneSENSE_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/oneSENSE_1.14.0.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: 2.0.0 Imports: stats, Rcpp, RcppProgress, dplyr, tidyr, ggplot2, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: 8743562f4e45794a903a474a0cec14de 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_13 git_last_commit: 94f9a83 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/onlineFDR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/onlineFDR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/onlineFDR_2.0.0.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: 1.14.0 Depends: R (>= 3.5), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown License: Artistic-2.0 MD5sum: 2eaff23b5e46079dcc8556218248b024 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_13 git_last_commit: 2ba1033 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ontoProc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ontoProc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ontoProc_1.14.0.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: 92 Package: openCyto Version: 2.4.0 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, testthat, utils, tools, parallel, ggcyto, CytoML License: Artistic-2.0 MD5sum: 282fbbe81bc93e51e015daed41339f94 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 ,Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_13 git_last_commit: 458a40d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/openCyto_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openCyto_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openCyto_2.4.0.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: FALSE 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: 122 Package: openPrimeR Version: 1.14.0 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: f0edb69fcc48c03e90c3beacb2c5bdf1 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_13 git_last_commit: cc9c24b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/openPrimeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openPrimeR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openPrimeR_1.14.0.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: 117 Package: openPrimeRui Version: 1.14.0 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 MD5sum: 9f0ee90d606d74fd9fe124710dae776a 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_13 git_last_commit: 3c0ba8d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/openPrimeRui_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openPrimeRui_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openPrimeRui_1.14.0.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: 139 Package: OpenStats Version: 1.4.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 82ebe92e3a3ff7ddd4ec18d6d997b78e 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_13 git_last_commit: b63d9ac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OpenStats_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OpenStats_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OpenStats_1.4.0.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: 134 Package: oposSOM Version: 2.10.0 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) Archs: i386, x64 MD5sum: 74e2df039bc1488e80de39e9054ac298 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_13 git_last_commit: 8a2b436 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oposSOM_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oposSOM_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oposSOM_2.10.0.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: 1.20.0 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: a8ca8e369a68a3a250d7f62a050841fc 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_13 git_last_commit: 152bfed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oppar_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oppar_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oppar_1.20.0.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: 79 Package: oppti Version: 1.6.0 Depends: R (>= 3.6) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools License: MIT Archs: i386, x64 MD5sum: e18167dc449a0de2329cc5976c2f1a4b 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_13 git_last_commit: db1f27a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/oppti_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oppti_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oppti_1.6.0.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: 99 Package: optimalFlow Version: 1.4.0 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 MD5sum: 475cf1b1aecd720ed01c80ab556ec8b4 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_13 git_last_commit: 2b1d6ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/optimalFlow_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/optimalFlow_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/optimalFlow_1.4.0.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: 91 Package: OPWeight Version: 1.14.0 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: 7d3703c2c8bb899fa4ab0b32438a6a8a 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_13 git_last_commit: be45b17 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OPWeight_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OPWeight_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OPWeight_1.14.0.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: 45 Package: OrderedList Version: 1.64.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: 3eb5dec3e409d7e30d5bca4d273b311b 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_13 git_last_commit: 3c2cc23 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OrderedList_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OrderedList_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OrderedList_1.64.0.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: 10 Package: ORFhunteR Version: 1.0.0 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 Archs: i386, x64 MD5sum: 436c80ee65506c8b4dea9889a5a1a973 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_13 git_last_commit: aacc377 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ORFhunteR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ORFhunteR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ORFhunteR_1.0.0.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: 55 Package: ORFik Version: 1.12.13 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), 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), GGally (>= 1.4.0), httr (>= 1.3.0), 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 Archs: i386, x64 MD5sum: f789694a425293bc33571ed824759892 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_13 git_last_commit: b86b7a7 git_last_commit_date: 2021-09-29 Date/Publication: 2021-09-30 source.ver: src/contrib/ORFik_1.12.13.tar.gz win.binary.ver: bin/windows/contrib/4.1/ORFik_1.12.13.zip mac.binary.ver: bin/macosx/contrib/4.1/ORFik_1.12.13.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 dependencyCount: 141 Package: Organism.dplyr Version: 1.20.0 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 MD5sum: d9a7c3f8d62287487956d14a03b618ba 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_13 git_last_commit: 1806248 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Organism.dplyr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Organism.dplyr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Organism.dplyr_1.20.0.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: 98 Package: OrganismDbi Version: 1.34.0 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 MD5sum: ca8131a15bb425762a7d616d8930735a 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_13 git_last_commit: 253c4ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OrganismDbi_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OrganismDbi_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OrganismDbi_1.34.0.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: 99 Package: OSAT Version: 1.40.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: e94e281d75d84abf0d150453dcba150d 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_13 git_last_commit: 722835e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OSAT_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OSAT_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OSAT_1.40.0.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: 1.22.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 71842c132082eddf777d421f4167756c 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_13 git_last_commit: 6dc2f86 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Oscope_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Oscope_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Oscope_1.22.0.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: 56 Package: OTUbase Version: 1.42.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 9a8bd816b62eefb83617f61cf39195f4 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_13 git_last_commit: d423dfc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OTUbase_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OTUbase_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OTUbase_1.42.0.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: 51 Package: OUTRIDER Version: 1.10.0 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 Archs: i386, x64 MD5sum: 0b5e8fd042687740dc2f65a2b3bfb0b0 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_13 git_last_commit: c9c57ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OUTRIDER_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OUTRIDER_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OUTRIDER_1.10.0.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: 1.8.0 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: d2f7ad4b460a34ccb224669ed23a56bf 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_13 git_last_commit: b69abfe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/OVESEG_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OVESEG_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OVESEG_1.8.0.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: 36 Package: PAA Version: 1.26.0 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 MD5sum: 69512b90d3428d9ff591506d007af5f8 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_13 git_last_commit: 39a0f50 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-27 source.ver: src/contrib/PAA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAA_1.26.0.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: 82 Package: packFinder Version: 1.4.0 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 MD5sum: c0bcca213c3e91b78e1afbe88fd85f38 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_13 git_last_commit: a750a91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/packFinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/packFinder_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/packFinder_1.4.0.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: 1.2.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot License: GPL (>=3) MD5sum: e3c6b5ab036f81566916012d3e210fe4 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_13 git_last_commit: 7bac95f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/padma_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/padma_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/padma_1.2.0.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: 129 Package: PADOG Version: 1.34.0 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: dde8f740fcd7c77cb72c02012794959a 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_13 git_last_commit: 66c89e4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PADOG_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PADOG_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PADOG_1.34.0.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: 1.2.0 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 Archs: i386, x64 MD5sum: 9659c74fd408d7ac85a7befb7cddf60f 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_13 git_last_commit: a54ad76 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pageRank_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pageRank_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pageRank_1.2.0.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: 1.8.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: ea773c2dc96e801dd5c804e53d95e743 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_13 git_last_commit: 90a8c7b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PAIRADISE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAIRADISE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAIRADISE_1.8.0.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: 35 Package: paircompviz Version: 1.30.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) Archs: i386, x64 MD5sum: b1e2aa280841b1a48becacae4d5b12e7 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_13 git_last_commit: c2347e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/paircompviz_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/paircompviz_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/paircompviz_1.30.0.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: 11 Package: pandaR Version: 1.24.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr License: GPL-2 MD5sum: 3bf52a4e8475ffaeda9bb6ca3923c973 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_13 git_last_commit: 930685b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pandaR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pandaR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pandaR_1.24.0.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 dependencyCount: 48 Package: panelcn.mops Version: 1.14.0 Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: b5af1c981fa0920ed0dadd81a89f8b84 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, Gundula Povysil Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_13 git_last_commit: 88ac1dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/panelcn.mops_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/panelcn.mops_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/panelcn.mops_1.14.0.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: 32 Package: panp Version: 1.62.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 9768fafa2fe9d91664516a24c7221b13 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_13 git_last_commit: 77c31f2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/panp_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/panp_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/panp_1.62.0.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: 13 Package: PANR Version: 1.38.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 5684df1dbf5871ed5f674e75e319ec33 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_13 git_last_commit: e07690c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PANR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PANR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PANR_1.38.0.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: 14 Package: PanVizGenerator Version: 1.20.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: eb81da19ab7da5bc00ebdc3a76404f4e NeedsCompilation: no Title: Generate PanViz visualisations from your pangenome Description: PanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed. biocViews: ComparativeGenomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/PanVizGenerator VignetteBuilder: knitr BugReports: https://github.com/thomasp85/PanVizGenerator/issues git_url: https://git.bioconductor.org/packages/PanVizGenerator git_branch: RELEASE_3_13 git_last_commit: 8eb2920 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PanVizGenerator_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PanVizGenerator_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PanVizGenerator_1.20.0.tgz vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html vignetteTitles: Creating PanViz visualizations with PanVizGenerator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R dependencyCount: 99 Package: parglms Version: 1.24.1 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 052e4d8f797ec1e0f8eac2cf645ad9af 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_13 git_last_commit: 0ffcdf6 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/parglms_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/parglms_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.1/parglms_1.24.1.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: 1.50.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 70cc02348ffd2f04fa353de017aa1429 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_13 git_last_commit: 7982ddb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/parody_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/parody_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/parody_1.50.0.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: 1.8.0 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: 70bbe7fd8b3754acc64383e766988c68 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_13 git_last_commit: f595850 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PAST_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAST_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAST_1.8.0.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: 87 Package: Path2PPI Version: 1.22.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 4b0027a53f688777462f57044cd44af5 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_13 git_last_commit: 0ce380a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Path2PPI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Path2PPI_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Path2PPI_1.22.0.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: 11 Package: pathifier Version: 1.30.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: e3eac28a168eb673142af2eef9b9b07b 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_13 git_last_commit: 39dcfa0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pathifier_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathifier_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathifier_1.30.0.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 importsMe: lilikoi dependencyCount: 9 Package: PathNet Version: 1.32.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 0fbbc2e4e65b4ea3afbad0f1bdc8256c 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_13 git_last_commit: 5110ddc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PathNet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PathNet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PathNet_1.32.0.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: 1.18.0 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: bee74a86f63da811d946b1851b024736 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_13 git_last_commit: 9d6981f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PathoStat_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PathoStat_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PathoStat_1.18.0.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: 206 Package: pathRender Version: 1.60.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 8283b3d982ce8da34210ceac192f8a8b 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_13 git_last_commit: a976baf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pathRender_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathRender_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathRender_1.60.0.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: 51 Package: pathVar Version: 1.22.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 1fff1a0e6d801e1dbdea3e552f913d19 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_13 git_last_commit: 44f759a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pathVar_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathVar_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathVar_1.22.0.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: 43 Package: pathview Version: 1.32.0 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: e65d36d55f4b619f22a498596962e5c0 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_13 git_last_commit: ec7e0e1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pathview_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathview_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathview_1.32.0.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, RNASeqR, SBGNview importsMe: CompGO, debrowser, EnrichmentBrowser, GDCRNATools, TCGAbiolinksGUI, TCGAWorkflow, lilikoi suggestsMe: gage, MAGeCKFlute, TCGAbiolinks, gageData, CAGEWorkflow dependencyCount: 52 Package: pathwayPCA Version: 1.8.0 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: 3cf4334e69e7d7fff8b116698b042179 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_13 git_last_commit: 4ab8ed1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pathwayPCA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathwayPCA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathwayPCA_1.8.0.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: 1.26.0 Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler License: LGPL-3 MD5sum: 03f0dfcc95b2d4884a31d96a0eddccbc NeedsCompilation: no Title: PaxtoolsR: 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 that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). 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 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_13 git_last_commit: 7635a83 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/paxtoolsr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/paxtoolsr_1.26.0.zip 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: 2.18.0 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: dc98c4421449b20754c7e751635531ac 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_13 git_last_commit: 724482a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pcaExplorer_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcaExplorer_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pcaExplorer_2.18.0.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: 1.84.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 8c46bb0890690c684a2eec91e55c8149 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_13 git_last_commit: 100c80e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pcaMethods_1.84.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcaMethods_1.84.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pcaMethods_1.84.0.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, CompGO, consensusDE, FRASER, MatrixQCvis, MSnbase, MSPrep, OUTRIDER, PanVizGenerator, PhosR, pmp, scde, SomaticSignatures, ADAPTS, geneticae, LOST, MetabolomicsBasics, missCompare, multiDimBio, polyRAD, RAMClustR, santaR, scMappR suggestsMe: MsCoreUtils, QFeatures, mtbls2, pagoda2 dependencyCount: 10 Package: PCAN Version: 1.20.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 658b393cc0ce0bacd7a0667c3ff73f4e 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_13 git_last_commit: c07417b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PCAN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PCAN_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PCAN_1.20.0.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: 12 Package: PCAtools Version: 2.4.0 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 MD5sum: ab8726a904cc43d5e5fea060c7d09f9f 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_13 git_last_commit: a3863ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PCAtools_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PCAtools_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PCAtools_2.4.0.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: 70 Package: pcxn Version: 2.14.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 5404bfd70ec3290894c7fcf4c214f763 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_13 git_last_commit: 992d550 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pcxn_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcxn_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pcxn_2.14.0.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: 20 Package: PDATK Version: 1.0.2 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 MD5sum: 46112ea3f208daf127a203c98d66e7b7 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], 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_13 git_last_commit: 4065ec5 git_last_commit_date: 2021-06-23 Date/Publication: 2021-06-24 source.ver: src/contrib/PDATK_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/PDATK_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/PDATK_1.0.2.tgz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP_model_analysis.html, 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: 258 Package: pdInfoBuilder Version: 1.56.0 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: 7a5cf9b165e30152a04f25158081cb3a 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_13 git_last_commit: 9bbe14d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pdInfoBuilder_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pdInfoBuilder_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pdInfoBuilder_1.56.0.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: 54 Package: PeacoQC Version: 1.2.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) Archs: i386, x64 MD5sum: 0dfda5730c25b71ddd0d0625f4dcaad8 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_13 git_last_commit: 0069897 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PeacoQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PeacoQC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PeacoQC_1.2.0.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: 1.6.1 Depends: R (>= 4.0) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 2.2.1), 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), shinythemes (>= 1.1.1), shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils Suggests: testthat, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 448e99dce00d3cdc871556e4f9b4b336 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_13 git_last_commit: 3308eac git_last_commit_date: 2021-06-23 Date/Publication: 2021-06-24 source.ver: src/contrib/peakPantheR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/peakPantheR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/peakPantheR_1.6.1.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: 1.28.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) Archs: i386, x64 MD5sum: 308a5aa91e29d6f9df3513832a04cb87 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_13 git_last_commit: 08d2016 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PECA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PECA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PECA_1.28.0.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: 1.4.0 Depends: R (>= 2.10) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: c3ba0460f2d17436dd60f494e3c4cf9f 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_13 git_last_commit: 32214ef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/peco_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/peco_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/peco_1.4.0.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: 95 Package: PepsNMR Version: 1.10.1 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: dee989f0be66797b059e524e63d0afcd 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_13 git_last_commit: 4a75865 git_last_commit_date: 2021-08-02 Date/Publication: 2021-08-03 source.ver: src/contrib/PepsNMR_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/PepsNMR_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/PepsNMR_1.10.1.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: 48 Package: pepStat Version: 1.26.0 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 Archs: i386, x64 MD5sum: 2a9d65b6bbcd17f37bd1adf1406ce6af 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_13 git_last_commit: a3472cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pepStat_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pepStat_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pepStat_1.26.0.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: 62 Package: pepXMLTab Version: 1.26.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ec1ca84cb7655c63a5e40b34b5456dc9 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_13 git_last_commit: 0b4e195 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pepXMLTab_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pepXMLTab_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pepXMLTab_1.26.0.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: 1.6.0 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: f6a07b7a6d6f95aecdb0b2f44729dfa9 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_13 git_last_commit: 65e5dbb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PERFect_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PERFect_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PERFect_1.6.0.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: 90 Package: periodicDNA Version: 1.2.0 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: 625b2033b41bdd894d2d328697dd8796 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_13 git_last_commit: 0a265ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/periodicDNA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/periodicDNA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/periodicDNA_1.2.0.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: perturbatr Version: 1.12.0 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 MD5sum: 2300078744d8ac59f122d3e6679dfacc NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/perturbatr git_branch: RELEASE_3_13 git_last_commit: 43f04ea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/perturbatr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/perturbatr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/perturbatr_1.12.0.tgz vignettes: vignettes/perturbatr/inst/doc/perturbatr.html vignetteTitles: perturbatr cookbook hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/perturbatr/inst/doc/perturbatr.R dependencyCount: 62 Package: PFP Version: 1.0.0 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: 1b2955d6cb69886a95158764eab245c8 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_13 git_last_commit: 80a0d85 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PFP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PFP_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PFP_1.0.0.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: 130 Package: pgca Version: 1.16.0 Imports: utils, stats Suggests: knitr, testthat License: GPL (>= 2) MD5sum: ad3e5eaccc28f138193e7f617c0b6689 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_13 git_last_commit: fd2bcd1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pgca_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pgca_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pgca_1.16.0.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: 1.12.0 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, curl Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table License: MIT + file LICENSE MD5sum: b16b3606c7d8d58b5677ef250d7ebd41 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_13 git_last_commit: e2115e2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phantasus_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phantasus_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phantasus_1.12.0.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: 145 Package: PharmacoGx Version: 2.4.0 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, glue Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown License: Artistic-2.0 MD5sum: 789a8dd734ee995d78249cd261ed8add NeedsCompilation: no 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_13 git_last_commit: 1565ba6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PharmacoGx_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PharmacoGx_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PharmacoGx_2.4.0.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: 131 Package: phemd Version: 1.8.0 Depends: R (>= 3.5), monocle Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, Seurat, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase, phateR, reticulate Suggests: knitr License: GPL-2 MD5sum: 8541e8adcac2a16878fe7ec766577bec 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 Maintainer: William S Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phemd git_branch: RELEASE_3_13 git_last_commit: 6c77cb3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phemd_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phemd_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phemd_1.8.0.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: 184 Package: PhenoGeneRanker Version: 1.0.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: 1d63f3040d5f45c806e5a508c67b1ae7 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_13 git_last_commit: 2cbd92c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PhenoGeneRanker_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhenoGeneRanker_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhenoGeneRanker_1.0.0.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: 32 Package: phenopath Version: 1.16.0 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: 2d2739d1f7beb4a7bf6f85877874cbf9 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_13 git_last_commit: de82be6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phenopath_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phenopath_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phenopath_1.16.0.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: 63 Package: phenoTest Version: 1.40.0 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: 3fa53bc402f3f43a69d4a315fa54659b 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_13 git_last_commit: 2fbc5e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phenoTest_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phenoTest_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phenoTest_1.40.0.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: 139 Package: PhenStat Version: 2.28.0 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 MD5sum: 66042ebf2012408bb0629fd662020f72 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_13 git_last_commit: d1496a6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PhenStat_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhenStat_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhenStat_2.28.0.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: 114 Package: philr Version: 1.18.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet, dplyr License: GPL-3 MD5sum: 69c2bb6966acb47732a452ad587de901 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 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_13 git_last_commit: 5dd64f6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/philr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/philr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/philr_1.18.0.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: 63 Package: PhIPData Version: 1.0.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr License: MIT + file LICENSE MD5sum: b6fbb6629d9419c0d16e4b387d63e016 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_13 git_last_commit: 988e473 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PhIPData_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhIPData_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhIPData_1.0.0.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 dependencyCount: 32 Package: phosphonormalizer Version: 1.16.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: f74bc4b0fc3da39cd114be57dfd28eeb 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_13 git_last_commit: 70319d9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phosphonormalizer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phosphonormalizer_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phosphonormalizer_1.16.0.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: 1.2.0 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 Archs: i386, x64 MD5sum: e084d4207b41a2dcb13827f359c0c7b6 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_13 git_last_commit: cd5ab36 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PhosR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhosR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhosR_1.2.0.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: 149 Package: PhyloProfile Version: 1.6.6 Depends: R (>= 4.1.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, RCurl, shiny, shinyBS, shinyjs, OmaDB, plyr, xml2, zoo Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 818046c5b592fea64a0425ba30c8461f 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_13 git_last_commit: 92e2e9e git_last_commit_date: 2021-06-29 Date/Publication: 2021-07-01 source.ver: src/contrib/PhyloProfile_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhyloProfile_1.6.6.zip mac.binary.ver: bin/macosx/contrib/4.1/PhyloProfile_1.6.6.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: 138 Package: phyloseq Version: 1.36.0 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: 5ccca0a61e66d256db1fc40074261b8f 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_13 git_last_commit: f9af643 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/phyloseq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phyloseq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phyloseq_1.36.0.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: ANCOMBC, combi, metavizr, microbiomeDASim, PathoStat, PERFect, RCM, reconsi, RPA, SPsimSeq, HMP2Data, adaptiveGPCA, corncob, HTSSIP, microbial, mixKernel, SigTree, treeDA suggestsMe: decontam, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, file2meco, metacoder, PLNmodels dependencyCount: 76 Package: Pi Version: 2.4.0 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: 0210029f8674b6a748d4451ae9732b75 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_13 git_last_commit: e5fb99d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Pi_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pi_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Pi_2.4.0.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: 140 Package: piano Version: 2.8.0 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: 789531f2ea7d96e9978fc0eac466542b 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_13 git_last_commit: f9697f5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/piano_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/piano_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/piano_2.8.0.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: BloodCancerMultiOmics2017 dependencyCount: 93 Package: pickgene Version: 1.64.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 81d6bac84fae39a36fca1c0271950870 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_13 git_last_commit: 94e97ec git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pickgene_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pickgene_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pickgene_1.64.0.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: 2.36.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 MD5sum: 978b26683e09adce6162abc7dc938f1a 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_13 git_last_commit: 87607d0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PICS_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PICS_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PICS_2.36.0.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: 38 Package: Pigengene Version: 1.18.10 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 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: fbb3979a2ca12bb5c606e76cd9cd1044 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, and Meghan Short Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_13 git_last_commit: 72e780a git_last_commit_date: 2021-09-27 Date/Publication: 2021-09-28 source.ver: src/contrib/Pigengene_1.18.10.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pigengene_1.18.10.zip mac.binary.ver: bin/macosx/contrib/4.1/Pigengene_1.18.10.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: 133 Package: PING Version: 2.36.0 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 MD5sum: 56577c708ad1865f5270a82b3a11dd4c 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_13 git_last_commit: 02aecc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PING_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PING_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PING_2.36.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 162 Package: pipeComp Version: 1.2.0 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: 5593a60346eda32493ac2740733aecbc 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_13 git_last_commit: b8a4bed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pipeComp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pipeComp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pipeComp_1.2.0.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: 203 Package: pipeFrame Version: 1.8.0 Depends: R (>= 3.6.1), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 MD5sum: 3269c60b719b00c47c94397134984d21 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_13 git_last_commit: e733729 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pipeFrame_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pipeFrame_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pipeFrame_1.8.0.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: 54 Package: pkgDepTools Version: 1.58.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 MD5sum: 7e4fc9f2abc2bd4a0f040a2da2d7fc7e 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_13 git_last_commit: 706a7e5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pkgDepTools_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pkgDepTools_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pkgDepTools_1.58.0.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: 10 Package: planet Version: 1.0.0 Depends: R (>= 4.0) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 385afeadea173b9a9c2a2e737671e176 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_13 git_last_commit: dbb7a2a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/planet_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/planet_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/planet_1.0.0.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 dependencyCount: 21 Package: plethy Version: 1.30.0 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: bf603b9888283f47bfa9522dabe4cb35 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_13 git_last_commit: fa08dc1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/plethy_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plethy_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plethy_1.30.0.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: 63 Package: plgem Version: 1.64.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 Archs: i386, x64 MD5sum: 4a7ffd8032e65f3c48b8be787bf3dcdc 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_13 git_last_commit: a7b40ff git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/plgem_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plgem_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plgem_1.64.0.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: 9 Package: plier Version: 1.62.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: 545bb6c056d9a54e12e1314a321789bc 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_13 git_last_commit: 405f847 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/plier_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plier_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plier_1.62.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 13 Package: PloGO2 Version: 1.4.0 Depends: R (>= 4.0), GO.db, GOstats Imports: lattice, httr, openxlsx, xtable License: GPL-2 MD5sum: 3e7c0fc20bb1f3703001b28175d4b055 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_13 git_last_commit: ca5de78 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PloGO2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PloGO2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PloGO2_1.4.0.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: 67 Package: plotGrouper Version: 1.10.0 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: f037c45d3517d904e492917ef14bb3ed 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_13 git_last_commit: 0004bdf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/plotGrouper_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plotGrouper_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plotGrouper_1.10.0.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: 141 Package: PLPE Version: 1.52.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 4dd055e1c53be275798214d59e325e3d 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_13 git_last_commit: 0a5e31d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PLPE_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PLPE_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PLPE_1.52.0.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: 10 Package: plyranges Version: 1.12.1 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: 8fdb2f859b880b63204e2a639224d5b3 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_13 git_last_commit: e3563e8 git_last_commit_date: 2021-06-27 Date/Publication: 2021-06-29 source.ver: src/contrib/plyranges_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/plyranges_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/plyranges_1.12.1.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: BUSpaRse, dasper, InPAS, methylCC, multicrispr, nearBynding, fluentGenomics suggestsMe: memes dependencyCount: 61 Package: pmm Version: 1.24.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: 55780ca7b526ec1a5b58a6df69315448 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_13 git_last_commit: 3bbc4d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pmm_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pmm_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pmm_1.24.0.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: 18 Package: pmp Version: 1.4.0 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 Archs: i386, x64 MD5sum: 80fe7c817bf9521541623c110667bfd2 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_13 git_last_commit: e0310dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pmp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pmp_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pmp_1.4.0.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: 1.0.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, testthat License: GPL-3 MD5sum: f1a5e11d607baa8567d3af3b3f67cf10 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] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: RELEASE_3_13 git_last_commit: d410b26 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PoDCall_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PoDCall_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PoDCall_1.0.0.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: 85 Package: podkat Version: 1.24.0 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: a95c503c337b2c1ade4c5bc15d424e12 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_13 git_last_commit: 01fa5e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/podkat_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/podkat_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/podkat_1.24.0.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: 46 Package: pogos Version: 1.12.2 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 License: Artistic-2.0 MD5sum: 316f0f49351dee9356bed1bc12be1a62 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_13 git_last_commit: 2735342 git_last_commit_date: 2021-08-29 Date/Publication: 2021-08-31 source.ver: src/contrib/pogos_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/pogos_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.1/pogos_1.12.2.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: 108 Package: polyester Version: 1.28.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 92b3f4d340aedcef4e553851967d7675 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_13 git_last_commit: a71cce4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/polyester_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/polyester_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/polyester_1.28.0.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: 21 Package: POMA Version: 1.2.0 Depends: R (>= 4.0) Imports: broom, caret, clisymbols, ComplexHeatmap, crayon, dplyr, e1071, ggcorrplot, ggplot2, ggraph, ggrepel, glasso (>= 1.11), glmnet, impute, knitr, limma, magrittr, mixOmics, MSnbase (>= 2.12), patchwork, qpdf, randomForest, RankProd (>= 3.14), rmarkdown, tibble, tidyr, vegan Suggests: Biobase, BiocStyle, covr, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: bb7d1b259c90d9b5913fa9ff169e9a22 NeedsCompilation: no Title: User-friendly Workflow for Metabolomics and Proteomics Data Analysis Description: A structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploratory data analysis, and statistical analysis of metabolomics and proteomics data. 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, Visualization, Preprocessing, Normalization, ReportWriting Author: Pol Castellano-Escuder [aut, cre] (), Cristina Andrés-Lacueva [aut] (), Alex Sánchez-Pla [aut] () 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_13 git_last_commit: a990e43 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/POMA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/POMA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/POMA_1.2.0.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: 168 Package: PoTRA Version: 1.8.2 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL MD5sum: 367140953c28886fb4ebed313d8dbe7b 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_13 git_last_commit: cf90958 git_last_commit_date: 2021-07-19 Date/Publication: 2021-07-20 source.ver: src/contrib/PoTRA_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/PoTRA_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/PoTRA_1.8.2.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: 57 Package: powerTCR Version: 1.12.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 322d4e107fb82c8a3ef36f137587cdd3 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_13 git_last_commit: 7031cbb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/powerTCR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/powerTCR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/powerTCR_1.12.0.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: 1.0.0 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 Archs: i386, x64 MD5sum: e277f0b1a3a20e95f456a8fdd3788b46 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_13 git_last_commit: f67097a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/POWSC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/POWSC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/POWSC_1.0.0.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: 1.0.0 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: a099813690035caa49521425d81458a3 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_13 git_last_commit: f8b106a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ppcseq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ppcseq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ppcseq_1.0.0.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: 101 Package: PPInfer Version: 1.18.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData License: Artistic-2.0 MD5sum: fafb3e68f3e3e126f8a8294f6099e90b 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_13 git_last_commit: 7713f20 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/PPInfer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PPInfer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PPInfer_1.18.0.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: 116 Package: ppiStats Version: 1.58.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: fa4c2018af7a3223b327bb7a89e3c929 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 git_url: https://git.bioconductor.org/packages/ppiStats git_branch: RELEASE_3_13 git_last_commit: 0d9ffa4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ppiStats_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ppiStats_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ppiStats_1.58.0.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: 69 Package: pqsfinder Version: 2.8.0 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.69.0) Suggests: BiocStyle, knitr, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE MD5sum: 91060034bc46b5ffc9f2c6d149e79223 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_13 git_last_commit: 777bb64 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pqsfinder_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pqsfinder_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pqsfinder_2.8.0.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: 22 Package: pram Version: 1.8.0 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: e461ed4ac45440e9de67c5b110617d0d 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_13 git_last_commit: 3e967c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pram_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pram_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pram_1.8.0.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: 45 Package: prebs Version: 1.32.0 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 MD5sum: 4c042c2cf9715532102eef7974091dee 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_13 git_last_commit: 67c681b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/prebs_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/prebs_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/prebs_1.32.0.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: 97 Package: preciseTAD Version: 1.2.0 Depends: R (>= 4.0.0) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, stats Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 4fb635344687d590459a6eaa31d2877d 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, cre], Mikhail Dozmorov [aut] Maintainer: Spiro Stilianoudakis 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_13 git_last_commit: c908e5e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/preciseTAD_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/preciseTAD_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/preciseTAD_1.2.0.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: 98 Package: PrecisionTrialDrawer Version: 1.8.0 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: 2763ab89fc974a7d3236c8d5dde0a11f 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: RELEASE_3_13 git_last_commit: 8690c66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PrecisionTrialDrawer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PrecisionTrialDrawer_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PrecisionTrialDrawer_1.8.0.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: 129 Package: PREDA Version: 1.38.0 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: 90ed462fc115af33ef59e81eee705d11 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_13 git_last_commit: f207bb8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PREDA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PREDA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PREDA_1.38.0.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: 58 Package: predictionet Version: 1.38.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 7c64b3c86730c2500c494886b70899c3 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg git_url: https://git.bioconductor.org/packages/predictionet git_branch: RELEASE_3_13 git_last_commit: 37c1515 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/predictionet_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/predictionet_1.38.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R dependencyCount: 24 Package: preprocessCore Version: 1.54.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 1dd8a41fb9ef6b5c819b463b01e81c85 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_13 git_last_commit: 66a30ca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/preprocessCore_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/preprocessCore_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/preprocessCore_1.54.0.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, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PECA, PhosR, Pigengene, proBatch, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict, RAMClustR, retriever, SMDIC, WGCNA suggestsMe: MsCoreUtils, multiClust, QFeatures, scp, splatter, aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.10.0 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: 95d11c2651b5aa8cb404f3dc1d9349c9 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_13 git_last_commit: e9cd880 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/primirTSS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/primirTSS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/primirTSS_1.10.0.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: 190 Package: PrInCE Version: 1.8.0 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 MD5sum: 43726299baa3e71f611ac393ea463415 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_13 git_last_commit: 0c2804f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PrInCE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PrInCE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PrInCE_1.8.0.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: 138 Package: proActiv Version: 1.2.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, Gviz, methods, rlang, S4Vectors, SummarizedExperiment, stats, tibble Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: 8316f17a34d6b7ea2800e08f09db8d39 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_13 git_last_commit: 0a66788 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proActiv_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proActiv_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proActiv_1.2.0.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: 149 Package: proBAMr Version: 1.26.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d833332419bf398f65e51b5a73fdc56f 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_13 git_last_commit: cf5184b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proBAMr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proBAMr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proBAMr_1.26.0.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: 96 Package: proBatch Version: 1.8.0 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: 05f7714d4cd3085fb3f7143cfcd947b7 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: RELEASE_3_13 git_last_commit: a577fe3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proBatch_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proBatch_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proBatch_1.8.0.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: 148 Package: PROcess Version: 1.68.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: c5f57457d23548e0f62cecd8531941d7 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_13 git_last_commit: bbbacba git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PROcess_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROcess_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROcess_1.68.0.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: 2.20.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 6730ad49c03faf80e170a8853bad1699 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_13 git_last_commit: 8884085 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/procoil_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/procoil_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/procoil_2.20.0.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: 31 Package: proDA Version: 1.6.0 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: b7d2a891084890707c20df3ed074cccc 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_13 git_last_commit: 73fced0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proDA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proDA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proDA_1.6.0.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: 28 Package: proFIA Version: 1.18.0 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: 202b49958d4f45df837c800b8a85adab 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_13 git_last_commit: ce749bb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proFIA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proFIA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proFIA_1.18.0.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: 104 Package: profileplyr Version: 1.8.1 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, Cairo, tiff, Rsamtools Suggests: BiocStyle, testthat, knitr, rmarkdown, png License: GPL (>= 3) MD5sum: 50559efdcd1eb7cacb903d338627ff37 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_13 git_last_commit: 7623d2f git_last_commit_date: 2021-08-09 Date/Publication: 2021-08-10 source.ver: src/contrib/profileplyr_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/profileplyr_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/profileplyr_1.8.1.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: 189 Package: profileScoreDist Version: 1.20.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: ad26b816f132151660c2bd2bf49906a2 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_13 git_last_commit: 1095a72 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/profileScoreDist_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/profileScoreDist_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/profileScoreDist_1.20.0.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: 7 Package: progeny Version: 1.14.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: 1b479122fe24d3fbb59ec470c24f71b8 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: This package provides a function to infer pathway activity from gene expression using PROGENy. It contains the linear model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression". biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [cre, ctb] (), Christian H. Holland [ctb] (), Igor Bulanov [ctb], Aurélien Dugourd [ctb] Maintainer: Alberto Valdeolivas 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_13 git_last_commit: 6252912 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/progeny_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/progeny_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/progeny_1.14.0.tgz vignettes: vignettes/progeny/inst/doc/progenyBulk.html, vignettes/progeny/inst/doc/ProgenySingleCell.html vignetteTitles: PROGENy pathway signatures: Application to Bulk transcriptomics, Applying PROGENy on single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progenyBulk.R, vignettes/progeny/inst/doc/ProgenySingleCell.R suggestsMe: mistyR dependencyCount: 50 Package: projectR Version: 1.8.0 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) MD5sum: c752e0e502f82018d7e0d67ed1991f77 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_13 git_last_commit: c86e61e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/projectR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/projectR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/projectR_1.8.0.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: 102 Package: pRoloc Version: 1.32.0 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: 6e8f2b00b5574a341f808460662cb152 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_13 git_last_commit: 637f4c7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pRoloc_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pRoloc_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pRoloc_1.32.0.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: pRolocGUI, proteomics suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 207 Package: pRolocGUI Version: 2.2.0 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, colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, shinydashboard, stats, grDevices, grid, BiocGenerics Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 815fd486dffcda5e2b929640642cb54a 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_13 git_last_commit: 685e93d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pRolocGUI_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pRolocGUI_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pRolocGUI_2.2.0.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: 220 Package: PROMISE Version: 1.44.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 8fe7840b94370fcd194d79a53242ff43 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_13 git_last_commit: ae08e9e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PROMISE_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROMISE_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROMISE_1.44.0.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: 51 Package: PROPER Version: 1.24.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL Archs: i386, x64 MD5sum: 0cefab8cd0b8de9c003f316b1f80853d 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_13 git_last_commit: 04d073c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PROPER_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROPER_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROPER_1.24.0.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: 1.14.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 7480b8dee78d01fa76357951c1f82914 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_13 git_last_commit: 042cf5f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PROPS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROPS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROPS_1.14.0.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: 75 Package: Prostar Version: 1.24.8 Depends: R (>= 4.1.0) Imports: DAPAR (>= 1.24.5), DAPARdata (>= 1.22.2), rhandsontable, data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, R.utils, shinythemes, XML,later, rclipboard, shinycssloaders, future, promises, colourpicker, BiocManager, shinyjqui,shinyTree, shinyWidgets, sass, tibble Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: d5c0fe52e729647c694a933a17ed3a75 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, ImmunoOncology, R.utils, GO, GUI Author: Samuel Wieczorek [cre, aut], Thomas Burger [aut], Enora Fremy [aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ BugReports: https://github.com/samWieczorek/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_13 git_last_commit: f1507c2 git_last_commit_date: 2021-08-21 Date/Publication: 2021-08-22 source.ver: src/contrib/Prostar_1.24.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/Prostar_1.24.8.zip mac.binary.ver: bin/macosx/contrib/4.1/Prostar_1.24.8.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: 323 Package: proteinProfiles Version: 1.32.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 Archs: i386, x64 MD5sum: 93fcce6ae6d58060d910b38fd311f3a1 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_13 git_last_commit: 92c9be6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/proteinProfiles_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proteinProfiles_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proteinProfiles_1.32.0.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: ProteomicsAnnotationHubData Version: 1.22.0 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 MD5sum: 8e74ee610e39e82c067c40cb43842c2e NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/ProteomicsAnnotationHubData git_branch: RELEASE_3_13 git_last_commit: f070c64 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ProteomicsAnnotationHubData_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProteomicsAnnotationHubData_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProteomicsAnnotationHubData_1.22.0.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html vignetteTitles: Proteomics Data in Annotation Hub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R dependencyCount: 169 Package: ProteoMM Version: 1.10.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 64c9fe8723bee1f9ab67033034dd1ff2 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_13 git_last_commit: 0521464 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ProteoMM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProteoMM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProteoMM_1.10.0.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 dependencyCount: 93 Package: ProtGenerics Version: 1.24.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 07e73a9c97addf4d99af00f4d7f9491a 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/lgatto/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_13 git_last_commit: 50e7e66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ProtGenerics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProtGenerics_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProtGenerics_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, Spectra, tofsims, topdownr importsMe: ensembldb, matter, MsBackendMassbank, MsFeatures, MSGFplus, MSnID, mzID, mzR, QFeatures, xcms dependencyCount: 1 Package: PSEA Version: 1.26.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: b078593db29e4ce343c3aec7760bf2da 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_13 git_last_commit: 9f69d31 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PSEA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PSEA_1.26.0.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: 9 Package: psichomics Version: 1.18.6 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: 92a127db88b09a5cd9b80fa572686f5b 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_13 git_last_commit: 2ffd219 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/psichomics_1.18.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/psichomics_1.18.6.zip mac.binary.ver: bin/macosx/contrib/4.1/psichomics_1.18.6.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: 203 Package: PSICQUIC Version: 1.30.0 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: 8ff9da1007880816ed7a117cb969d67b 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 git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_13 git_last_commit: 3d77738 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PSICQUIC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PSICQUIC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PSICQUIC_1.30.0.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: 73 Package: psygenet2r Version: 1.24.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr License: MIT + file LICENSE MD5sum: b94279f21051e274ec525a2620d7311e 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_13 git_last_commit: 7a013f8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/psygenet2r_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/psygenet2r_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/psygenet2r_1.24.0.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: 1.0.1 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: 051298e64f5629ec2faa9ae91cacde9a 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 single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection. biocViews: Software, MassSpectrometry, Preprocessing, Metabolomics, PeakDetection, Alignment Author: camille Roquencourt [aut, cre] Maintainer: camille Roquencourt VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: RELEASE_3_13 git_last_commit: 701b577 git_last_commit_date: 2021-09-27 Date/Publication: 2021-09-28 source.ver: src/contrib/ptairMS_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ptairMS_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ptairMS_1.0.1.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: 188 Package: PubScore Version: 1.4.0 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: bf094e8316bc8f110429a8b95203da71 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 git_url: https://git.bioconductor.org/packages/PubScore git_branch: RELEASE_3_13 git_last_commit: 5e46d7d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/PubScore_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PubScore_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PubScore_1.4.0.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: 64 Package: pulsedSilac Version: 1.6.0 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 MD5sum: 9311a33821dcfecffb4f3809a09446b0 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: RELEASE_3_13 git_last_commit: 33c3efa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pulsedSilac_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pulsedSilac_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pulsedSilac_1.6.0.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: 72 Package: puma Version: 3.34.0 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 Archs: i386, x64 MD5sum: 62ab124df86604ff17b50f9de00a496b 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_13 git_last_commit: 8863f78 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/puma_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/puma_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/puma_3.34.0.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: 56 Package: PureCN Version: 1.22.2 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, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, jsonlite, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 MD5sum: 24d24c1686aeb9110002fd07c0e4c407 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_13 git_last_commit: 1d595f4 git_last_commit_date: 2021-07-01 Date/Publication: 2021-07-04 source.ver: src/contrib/PureCN_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/PureCN_1.22.2.zip mac.binary.ver: bin/macosx/contrib/4.1/PureCN_1.22.2.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: 119 Package: pvac Version: 1.40.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 8899f92241d865ff3951ba7a04c79713 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_13 git_last_commit: 2500206 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pvac_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pvac_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pvac_1.40.0.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: 13 Package: pvca Version: 1.32.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: c434aecf981feb769c45dec3c83a959d 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_13 git_last_commit: 7a06e6c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pvca_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pvca_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pvca_1.32.0.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: 54 Package: Pviz Version: 1.26.0 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: ba715fc85dae76e6c9abb56631f351d7 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_13 git_last_commit: dcce10c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Pviz_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pviz_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Pviz_1.26.0.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: 142 Package: PWMEnrich Version: 4.28.1 Depends: R (>= 3.5.0), methods, grid, BiocGenerics, Biostrings Imports: 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: a6b765f59d51a0d1932c9a71fc8e4826 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_13 git_last_commit: 7ca4ee9 git_last_commit_date: 2021-05-25 Date/Publication: 2021-05-25 source.ver: src/contrib/PWMEnrich_4.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/PWMEnrich_4.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/PWMEnrich_4.28.1.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: 24 Package: pwOmics Version: 1.24.0 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) Archs: i386, x64 MD5sum: c09010e93d3a3d6fec681653787ebf63 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_13 git_last_commit: 9b1e369 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pwOmics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pwOmics_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pwOmics_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 116 Package: pwrEWAS Version: 1.6.0 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: 38ff9f5f016353818c0cf0bd32771bba 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_13 git_last_commit: 48c3ab7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/pwrEWAS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pwrEWAS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pwrEWAS_1.6.0.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: 122 Package: qckitfastq Version: 1.8.0 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: adee9fa23af7b0aee6aee2d689a8e6b7 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_13 git_last_commit: 6e68a18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qckitfastq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qckitfastq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qckitfastq_1.8.0.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: 52 Package: qcmetrics Version: 1.30.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle License: GPL-2 MD5sum: 1222688a063c9bf8a3ca78dd2d9e1f5c 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_13 git_last_commit: ba3f35c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qcmetrics_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qcmetrics_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qcmetrics_1.30.0.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: 24 Package: QDNAseq Version: 1.28.0 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 (>= 0.54.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future (>= 1.14.0), future.apply (>= 1.3.0) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: b754d3b0af4bf772106b1e2c80cc8201 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_13 git_last_commit: 3ca285f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QDNAseq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QDNAseq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QDNAseq_1.28.0.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: 1.2.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.19.3), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.1.2), Suggests: SingleCellExperiment, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm License: Artistic-2.0 MD5sum: 817b47db795215763ea73dfeb0c65a23 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_13 git_last_commit: e858c2b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QFeatures_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QFeatures_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QFeatures_1.2.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R dependsOnMe: msqrob2, scp, scpdata dependencyCount: 55 Package: qpcrNorm Version: 1.50.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 0e2dca0a3208efda256517f35cb44ce6 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_13 git_last_commit: 9538a20 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qpcrNorm_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qpcrNorm_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qpcrNorm_1.50.0.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: 14 Package: qpgraph Version: 2.26.0 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.0), 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: 400aad4dc4be7b00dff738355d10e40e 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_13 git_last_commit: 80062b5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qpgraph_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qpgraph_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qpgraph_2.26.0.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, topologyGSA dependencyCount: 102 Package: qPLEXanalyzer Version: 1.10.0 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 Archs: i386, x64 MD5sum: ea25b1fe9f0e3ec7aa22f6cfec12a5a4 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_13 git_last_commit: 1edf1ad git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qPLEXanalyzer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qPLEXanalyzer_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qPLEXanalyzer_1.10.0.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: 1.46.0 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) MD5sum: 5cb6ae5e4d44f9f39a0b67b9b18062a6 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_13 git_last_commit: 891dd79 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qrqc_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qrqc_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qrqc_1.46.0.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: 157 Package: qsea Version: 1.18.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel, KernSmooth, MASS Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager License: GPL (>=2) MD5sum: ea6d1d296558d3f212c7f7eb48fb00cb 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_13 git_last_commit: f23006b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qsea_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qsea_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qsea_1.18.0.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: 52 Package: qsmooth Version: 1.8.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 MD5sum: 5989be7d169c31df4aa9271b5ccf180a 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], 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_13 git_last_commit: 85b51b0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qsmooth_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qsmooth_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qsmooth_1.8.0.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: 73 Package: QSutils Version: 1.10.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE MD5sum: ea3bb2b6ae5cd784fa6ed413cb750dee 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_13 git_last_commit: 74dbca7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QSutils_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QSutils_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QSutils_1.10.0.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: 27 Package: Qtlizer Version: 1.6.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 219f5c3fe790be9612b54a29761b86e8 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_13 git_last_commit: 7da775a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Qtlizer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Qtlizer_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Qtlizer_1.6.0.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: 26 Package: quantiseqr Version: 1.0.0 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: 0d0ae8bcb7a80fd8a2268d5fcff2d706 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_13 git_last_commit: 4408bea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/quantiseqr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantiseqr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantiseqr_1.0.0.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 dependencyCount: 66 Package: quantro Version: 1.26.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) Archs: i386, x64 MD5sum: 65057a21fec4146761dbc396f65fbd74 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_13 git_last_commit: b93958c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/quantro_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantro_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantro_1.26.0.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: 150 Package: quantsmooth Version: 1.58.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 Archs: i386, x64 MD5sum: 4218abedc14f09aa3e0bc795cd6f6262 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_13 git_last_commit: 6446b0c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/quantsmooth_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantsmooth_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantsmooth_1.58.0.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: 15 Package: QuartPAC Version: 1.24.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: ee226e9e546083d3c1b44438617767d0 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_13 git_last_commit: 5debb20 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QuartPAC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuartPAC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuartPAC_1.24.0.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: 1.32.0 Depends: R (>= 4.0), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, BiocGenerics, S4Vectors, IRanges, BiocManager, Biobase, Biostrings, BSgenome, Rsamtools, GenomicFeatures, ShortRead, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, AnnotationDbi, tools LinkingTo: Rhtslib Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: 110512509111ecfe022e39d0a2724531 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_13 git_last_commit: b24d275 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QuasR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuasR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuasR_1.32.0.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: 106 Package: QuaternaryProd Version: 1.26.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: 076da9acaa60eac2ef2a2ba8309b092d 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_13 git_last_commit: 86cd5be git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/QuaternaryProd_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuaternaryProd_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuaternaryProd_1.26.0.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: 1.20.1 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 MD5sum: a3189dbbeb015579ec70b70d158d8ac7 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_13 git_last_commit: 0217483 git_last_commit_date: 2021-07-27 Date/Publication: 2021-07-29 source.ver: src/contrib/QUBIC_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/QUBIC_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/QUBIC_1.20.1.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 suggestsMe: runibic dependencyCount: 53 Package: qusage Version: 2.26.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: 9f4e20aa4e014ed5bdd0337c308d4b79 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_13 git_last_commit: 599ffb5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qusage_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qusage_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qusage_2.26.0.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 dependsOnMe: DrInsight importsMe: mExplorer suggestsMe: SigCheck dependencyCount: 18 Package: qvalue Version: 2.24.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: 414eba00a898b375b1ce0870c31cb8ce 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_13 git_last_commit: a64acae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/qvalue_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qvalue_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qvalue_2.24.0.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, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RiboDiPA, RNAsense, Rnits, SDAMS, sights, signatureSearch, subSeq, trigger, webbioc, IHWpaper, AEenrich, armada, cancerGI, DGEobj.utils, fdrDiscreteNull, glmmSeq, groupedSurv, jaccard, jackstraw, NBPSeq, SeqFeatR, ssizeRNA suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, easylabel, matR, mutoss, Rediscover, seqgendiff, wrMisc dependencyCount: 44 Package: R3CPET Version: 1.24.0 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: b7355090c26b15f4fe5b9f0c66e6d46a 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_13 git_last_commit: 577029f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/R3CPET_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R3CPET_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R3CPET_1.24.0.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: 157 Package: r3Cseq Version: 1.38.0 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 Archs: x64 MD5sum: 51b9ff25c910ff657113add7d91e9e2f 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_13 git_last_commit: f159c04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/r3Cseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/r3Cseq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/r3Cseq_1.38.0.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: 1.42.0 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: i386, x64 MD5sum: edfb53cc94b4e88737eb13552e25485b 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_13 git_last_commit: 02482b8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/R453Plus1Toolbox_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R453Plus1Toolbox_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R453Plus1Toolbox_1.42.0.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: 106 Package: R4RNA Version: 1.20.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 Archs: i386, x64 MD5sum: 0b2ef6501976971d80ea80736eb0d5a5 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_13 git_last_commit: d3707fc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/R4RNA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R4RNA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R4RNA_1.20.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R suggestsMe: rfaRm dependencyCount: 19 Package: RadioGx Version: 1.2.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, 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 MD5sum: 18ba8feaeda039e367fad1ed8e8d701d 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_13 git_last_commit: fdb4a45 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RadioGx_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RadioGx_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RadioGx_1.2.0.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: 132 Package: RaggedExperiment Version: 1.16.0 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: ca43bb0598ab1e28857155c2c3d42268 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_13 git_last_commit: a1c10f7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RaggedExperiment_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RaggedExperiment_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RaggedExperiment_1.16.0.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 suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, curatedTCGAData, SingleCellMultiModal dependencyCount: 26 Package: rain Version: 1.26.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: c79afd2b827d918d64ce368ed8c065f7 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_13 git_last_commit: 03a40f6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rain_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rain_1.26.0.zip 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: 17 Package: rama Version: 1.66.0 Depends: R(>= 2.5.0) License: GPL (>= 2) MD5sum: 7ee6e76082f305b188a58718e73e0cd5 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_13 git_last_commit: 8d29b43 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rama_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rama_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rama_1.66.0.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: 1.0.3 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 Archs: i386, x64 MD5sum: 7a373f8eabfded74ea2b7c788fd07cba 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_13 git_last_commit: 7bf9acb git_last_commit_date: 2021-08-13 Date/Publication: 2021-08-15 source.ver: src/contrib/ramr_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/ramr_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/ramr_1.0.3.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: 68 Package: ramwas Version: 1.16.0 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 MD5sum: 2c5da015b7562cfbd5226edb7075b9c7 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_13 git_last_commit: 239ad35 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ramwas_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ramwas_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ramwas_1.16.0.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: 99 Package: RandomWalkRestartMH Version: 1.12.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: bbe63a366139398db97e38e442589a90 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". https://www.biorxiv.org/content/early/2017/08/30/134734 . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas Urbelz Maintainer: Alberto Valdeolivas Urbelz URL: https://www.biorxiv.org/content/early/2017/08/30/134734 git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_13 git_last_commit: 0e1c73a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RandomWalkRestartMH_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RandomWalkRestartMH_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RandomWalkRestartMH_1.12.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.pdf vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.R dependencyCount: 54 Package: randPack Version: 1.38.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 233caac538598949d61c572dc9c3cfd4 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_13 git_last_commit: b9875fd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/randPack_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/randPack_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/randPack_1.38.0.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: 7 Package: randRotation Version: 1.4.0 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: e8c3dc391600580c31171637d20c59fa 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_13 git_last_commit: cf2c134 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/randRotation_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/randRotation_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/randRotation_1.4.0.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: 3.18.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 938364c7ab29d7ae889454035f5eafde 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_13 git_last_commit: 394f13f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RankProd_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RankProd_3.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RankProd_3.18.0.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, sigQC dependencyCount: 6 Package: RareVariantVis Version: 2.20.0 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: 6a05d74c6f68618a51af79e2e6d523e5 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_13 git_last_commit: 2e6f87c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RareVariantVis_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RareVariantVis_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RareVariantVis_2.20.0.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: 130 Package: rawrr Version: 1.0.3 Depends: R (>= 4.1) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.6), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 Archs: i386, x64 MD5sum: b609da350ee5071730ce95cba1563734 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_13 git_last_commit: 57ff81e git_last_commit_date: 2021-09-14 Date/Publication: 2021-09-16 source.ver: src/contrib/rawrr_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/rawrr_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/rawrr_1.0.3.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 dependencyCount: 4 Package: RbcBook1 Version: 1.60.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 2beab9248a1c09a560761948414a3780 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_13 git_last_commit: f563f7a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RbcBook1_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RbcBook1_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RbcBook1_1.60.0.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: 11 Package: Rbec Version: 1.0.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: b535d65f1ad72920538d01b80c1c8922 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_13 git_last_commit: 8dd1cc7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rbec_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbec_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbec_1.0.0.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: 95 Package: RBGL Version: 1.68.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0b158610548cfe967c20fddb67f66e58 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_13 git_last_commit: 9433738 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RBGL_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBGL_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBGL_1.68.0.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, archeofrag, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GAPGOM, GOSim, GOstats, MIGSA, NCIgraph, OrganismDbi, pkgDepTools, predictionet, RpsiXML, Streamer, VariantFiltering, BiDAG, eff2, gRbase, HEMDAG, netgwas, pcalg, rags2ridges, RANKS, SID, wiseR suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, gRc, maGUI dependencyCount: 9 Package: RBioinf Version: 1.52.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 MD5sum: a01408a2af3ade5fada9bc9d630356c0 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_13 git_last_commit: 4edfe70 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RBioinf_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBioinf_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBioinf_1.52.0.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: 8 Package: rBiopaxParser Version: 2.32.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: 58d4d1509bad4925397468157cda82e9 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_13 git_last_commit: ed5cf40 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rBiopaxParser_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rBiopaxParser_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rBiopaxParser_2.32.0.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: 1.24.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: e11e45d1431c56e6f76e12bc1e9ed0a3 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_13 git_last_commit: f2a8092 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RBM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBM_1.24.0.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: 1.32.0 Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 760690b36a0df89de021c4d4f98ac0a4 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_13 git_last_commit: 1a56c71 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rbowtie_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbowtie_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie_1.32.0.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: MACPET, multicrispr suggestsMe: eisaR dependencyCount: 0 Package: Rbowtie2 Version: 1.14.0 Depends: R (>= 3.5) Suggests: knitr License: GPL (>= 3) Archs: i386, x64 MD5sum: 05117fd43ec80d985054f28180e66709 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. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_13 git_last_commit: f15bc6e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rbowtie2_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbowtie2_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie2_1.14.0.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: 0 Package: rbsurv Version: 2.50.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) Archs: i386, x64 MD5sum: ba02c02c758c7d1027dd9078286db488 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_13 git_last_commit: bd8165b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rbsurv_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rbsurv_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rbsurv_2.50.0.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: 13 Package: Rcade Version: 1.34.0 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 Archs: i386, x64 MD5sum: e8039915bc93bc5ab61e749a97c91e8a 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: RELEASE_3_13 git_last_commit: d311ac7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rcade_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rcade_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rcade_1.34.0.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: 1.18.0 Depends: R (>= 3.3.0), 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 MD5sum: b61db623dadb9092911793d350f7364c 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_13 git_last_commit: ffebafc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RCAS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCAS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCAS_1.18.0.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: 152 Package: RCASPAR Version: 1.38.0 License: GPL (>=3) MD5sum: 058c2a9c63b836dc60c0d8e595dcefcf 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_13 git_last_commit: fa5ea8c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RCASPAR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCASPAR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCASPAR_1.38.0.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: 2.14.0 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 License: LGPL-3 + file LICENSE Archs: x64 MD5sum: a3996a2b0c2006cd0ed079b71a711a2f 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_13 git_last_commit: 65b993c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rcellminer_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rcellminer_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rcellminer_2.14.0.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: 1.22.0 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 Archs: x64 MD5sum: b876882ffcce83408b8fe829a19c7557 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_13 git_last_commit: 23845ef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rCGH_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rCGH_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rCGH_1.22.0.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 dependencyCount: 140 Package: RcisTarget Version: 1.12.0 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, 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: 79998857883f879dcc9ecacdc6e755bc 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_13 git_last_commit: da8bb0d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RcisTarget_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RcisTarget_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RcisTarget_1.12.0.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 regiions, 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: 101 Package: RCM Version: 1.8.0 Depends: R (>= 3.6.0) Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, stats, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 06262b9b1e96b071e61d1ece6293689a 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. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel Maintainer: Joris Meys 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_13 git_last_commit: b1cf974 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RCM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCM_1.8.0.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: 1.28.0 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 | file LICENSE MD5sum: 77e97ca511bbace0d45db1bc9922b99d NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: Rcpi offers a molecular informatics toolkit with a comprehensive 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_13 git_last_commit: 422fb52 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rcpi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rcpi_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rcpi_1.28.0.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: 100 Package: RCSL Version: 1.0.0 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: 110e206d5ca2f232936cf7097c0890cb 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_13 git_last_commit: 51b4de1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RCSL_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCSL_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCSL_1.0.0.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: 56 Package: Rcwl Version: 1.8.1 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: 7b1f0bf236c65fa5bd80fb23959694ef 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_13 git_last_commit: 04dd98c git_last_commit_date: 2021-06-29 Date/Publication: 2021-07-01 source.ver: src/contrib/Rcwl_1.8.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/Rcwl_1.8.1.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: 115 Package: RcwlPipelines Version: 1.8.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 9cb057b02cd082a95feaf7529db991fb 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_13 git_last_commit: 4e08b9e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RcwlPipelines_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RcwlPipelines_1.8.0.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: 130 Package: RCy3 Version: 2.12.4 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, igraph, stats, graph, R.utils, dplR, uchardet, glue, RCurl, base64url, base64enc, IRkernel, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 1d5cade6f792b43e0d759738bca6899b 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_13 git_last_commit: 5bb5011 git_last_commit_date: 2021-08-09 Date/Publication: 2021-08-10 source.ver: src/contrib/RCy3_2.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCy3_2.12.4.zip mac.binary.ver: bin/macosx/contrib/4.1/RCy3_2.12.4.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/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, 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/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, regutools, TimiRGeN, transomics2cytoscape, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, rScudo, sparsebnUtils dependencyCount: 64 Package: RCyjs Version: 2.14.0 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: 4d55c49172f728bf81a166e158d36b2c 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_13 git_last_commit: d93cc23 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RCyjs_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCyjs_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCyjs_2.14.0.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: 18 Package: rDGIdb Version: 1.18.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 0b443707d6ea816330ddfbe45bcac0c4 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_13 git_last_commit: 586ff45 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rDGIdb_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rDGIdb_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rDGIdb_1.18.0.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: 1.52.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 1027528cd90138e1b951427e564c65f4 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_13 git_last_commit: 2f422e0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rdisop_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rdisop_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rdisop_1.52.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: enviGCMS, HiResTEC, InterpretMSSpectrum, MetaDBparse suggestsMe: adductomicsR, MSnbase, RforProteomics dependencyCount: 3 Package: RDRToolbox Version: 1.42.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: df8ca3cf85f599659c6ff10186bdc280 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_13 git_last_commit: e16efe3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RDRToolbox_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RDRToolbox_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RDRToolbox_1.42.0.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: 27 Package: ReactomeContentService4R Version: 1.0.0 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 MD5sum: 07f9f703d348cbbd06ae6c580c9a872f 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_13 git_last_commit: 02b2833 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ReactomeContentService4R_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeContentService4R_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeContentService4R_1.0.0.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: 1.0.0 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: a95778fd920af8536d2e5fa885e54311 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_13 git_last_commit: 526e3dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ReactomeGraph4R_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeGraph4R_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGraph4R_1.0.0.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: 69 Package: ReactomeGSA Version: 1.6.1 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE MD5sum: 38717409f1ae023b0790a99e04684d0f 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_13 git_last_commit: 0fa14a1 git_last_commit_date: 2021-09-10 Date/Publication: 2021-09-23 source.ver: src/contrib/ReactomeGSA_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeGSA_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGSA_1.6.1.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: 54 Package: ReactomePA Version: 1.36.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: fddc702a3c4569a7d44efb200b20f632 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_13 git_last_commit: 78eb2e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ReactomePA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomePA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomePA_1.36.0.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, scTensor, ExpHunterSuite suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, scGPS dependencyCount: 130 Package: ReadqPCR Version: 1.38.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 Archs: i386, x64 MD5sum: aaf3540de3eff6451b550dbee2b541e4 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_13 git_last_commit: d387f65 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ReadqPCR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReadqPCR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReadqPCR_1.38.0.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: 7 Package: REBET Version: 1.10.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 416cbe93c0e2e09b3a2e96b5de61e609 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_13 git_last_commit: fc89bb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/REBET_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REBET_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REBET_1.10.0.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: 1.2.1 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: 88e285d04b040ebaa0bf088be1346d8a 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_13 git_last_commit: fd68600 git_last_commit_date: 2021-08-28 Date/Publication: 2021-08-29 source.ver: src/contrib/rebook_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rebook_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rebook_1.2.1.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: 36 Package: receptLoss Version: 1.4.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE MD5sum: 2c49e0beeaebdbbe2490f804ef1524f9 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_13 git_last_commit: 76bd045 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/receptLoss_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/receptLoss_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/receptLoss_1.4.0.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: 62 Package: reconsi Version: 1.4.0 Imports: phyloseq, KernSmooth, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 9ca7148d5f5b8cb2dc2f40013facd9b6 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 Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_13 git_last_commit: 4f305fe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/reconsi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/reconsi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/reconsi_1.4.0.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: 79 Package: recount Version: 1.18.1 Depends: R (>= 3.3.0), 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: 1c592a6bb5c3298aba8bed4cafd4c7ae 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_13 git_last_commit: 64a92e6 git_last_commit_date: 2021-08-09 Date/Publication: 2021-08-10 source.ver: src/contrib/recount_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/recount_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/recount_1.18.1.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, recount3 dependencyCount: 158 Package: recount3 Version: 1.2.6 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: fbf5ec3ebdc6d1af56477dc8f936588f 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_13 git_last_commit: b41fa0f git_last_commit_date: 2021-09-27 Date/Publication: 2021-09-28 source.ver: src/contrib/recount3_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/recount3_1.2.6.zip mac.binary.ver: bin/macosx/contrib/4.1/recount3_1.2.6.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 dependencyCount: 89 Package: recountmethylation Version: 1.2.3 Depends: R (>= 4.0.0) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache Suggests: knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 Archs: i386, x64 MD5sum: a3eefc03810def61b36b94bc6b72edbc NeedsCompilation: no Title: Access and analyze DNA methylation database compilations Description: Access cross-study compilations of DNA methylation array databases. Database files can be downloaded and accessed using provided functions. Background about database file types (HDF5 and HDF5-SummarizedExperiment), SummarizedExperiment classes, and examples for data handling, validation, and analyses, can be found in the package vignettes. Note the disclaimer on package load, and consult the main manuscript for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (), Reid F Thompson [aut] (), Kasper D Hansen [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_13 git_last_commit: 3557cec git_last_commit_date: 2021-10-11 Date/Publication: 2021-10-12 source.ver: src/contrib/recountmethylation_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/recountmethylation_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.1/recountmethylation_1.2.3.tgz vignettes: vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Data Analyses, 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_users_guide.R dependencyCount: 142 Package: recoup Version: 1.20.1 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: 02055c4a12a40772626f2b390e68a3d9 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_13 git_last_commit: ab6d090 git_last_commit_date: 2021-10-06 Date/Publication: 2021-10-07 source.ver: src/contrib/recoup_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/recoup_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/recoup_1.20.1.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: 122 Package: RedeR Version: 1.40.5 Depends: R (>= 3.5), methods Imports: igraph Suggests: pvclust, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: a7a744f788e9d7ea507da65a7be9d591 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 modular structures, nested networks and multiple levels of hierarchical associations. 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 (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_13 git_last_commit: f5bb912 git_last_commit_date: 2021-09-25 Date/Publication: 2021-09-26 source.ver: src/contrib/RedeR_1.40.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/RedeR_1.40.5.zip mac.binary.ver: bin/macosx/contrib/4.1/RedeR_1.40.5.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical network representation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf dependencyCount: 11 Package: REDseq Version: 1.38.0 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: 1a16f75bc097dcf0b5f494cdbcd54058 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_13 git_last_commit: 4c3b25a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/REDseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REDseq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REDseq_1.38.0.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: 124 Package: RefPlus Version: 1.62.0 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: b9f508c0383d99086600b547f33c94e6 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_13 git_last_commit: 9fed9a4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RefPlus_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RefPlus_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RefPlus_1.62.0.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: 27 Package: RegEnrich Version: 1.2.0 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: c7fbaf01d62af23e1f2c22e2190fd3a1 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_13 git_last_commit: 65372ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RegEnrich_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RegEnrich_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RegEnrich_1.2.0.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: 147 Package: regioneR Version: 1.24.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 14f99bc90bbd64747350e57a45480d36 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_13 git_last_commit: 3cbcdab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/regioneR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regioneR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regioneR_1.24.0.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, UMI4Cats suggestsMe: CNVRanger dependencyCount: 49 Package: regionReport Version: 1.26.0 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 (>= 1.3.2), 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: 156dc71053ca6e9c2540b5e10ab01888 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_13 git_last_commit: 985fa5a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/regionReport_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regionReport_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regionReport_1.26.0.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: 168 Package: regsplice Version: 1.18.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 3571d0ae3cfdeb096b84c10f87a462fa 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_13 git_last_commit: d2c7809 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/regsplice_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regsplice_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regsplice_1.18.0.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: 38 Package: regutools Version: 1.4.0 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: cef7cb481f9ae34d7a287fa28aad0a5a 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_13 git_last_commit: fe87b86 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/regutools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regutools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regutools_1.4.0.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: 177 Package: REMP Version: 1.16.0 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: dd8f75dfd8f98a100a906c7adbaa47ae 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_13 git_last_commit: 9be0aa3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/REMP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REMP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REMP_1.16.0.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: 203 Package: Repitools Version: 1.38.0 Depends: R (>= 3.0.0), 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: 44fe96822d6dea94cb33bcb9473b32ce 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_13 git_last_commit: dc767d1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Repitools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Repitools_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Repitools_1.38.0.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: 2.32.1 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 License: Artistic-2.0 MD5sum: 719c879c706aee4b0b3770c27adbd2b8 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_13 git_last_commit: 158e4a4 git_last_commit_date: 2021-07-26 Date/Publication: 2021-07-27 source.ver: src/contrib/ReportingTools_2.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReportingTools_2.32.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ReportingTools_2.32.1.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: 173 Package: RepViz Version: 1.8.0 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: knitr, testthat License: GPL-3 MD5sum: 6cb6c81098fd39b8eb1a88f469bb56f9 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_13 git_last_commit: 700aac6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RepViz_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RepViz_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RepViz_1.8.0.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: 82 Package: ReQON Version: 1.38.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: 3fb48a41689780090debbf2dc690592a 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_13 git_last_commit: 01c27aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ReQON_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReQON_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReQON_1.38.0.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: 31 Package: ResidualMatrix Version: 1.2.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 9ee1962a7b02ffd916eaa4af46eea880 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_13 git_last_commit: a4d7553 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ResidualMatrix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ResidualMatrix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ResidualMatrix_1.2.0.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: 16 Package: restfulSE Version: 1.14.2 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: a6f58ff9a60bb09af635611ed59eb0f2 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_13 git_last_commit: 9a05544 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/restfulSE_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/restfulSE_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/restfulSE_1.14.2.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: 110 Package: rexposome Version: 1.14.1 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 Archs: i386, x64 MD5sum: 7ace3ae903b10f14023b2b36b4ec7605 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_13 git_last_commit: 8a968e3 git_last_commit_date: 2021-07-12 Date/Publication: 2021-07-13 source.ver: src/contrib/rexposome_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rexposome_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rexposome_1.14.1.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: 157 Package: rfaRm Version: 1.4.3 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics License: GPL-3 MD5sum: 049de8fb214ed5b5fde0d1f793d1f009 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_13 git_last_commit: f034172 git_last_commit_date: 2021-08-04 Date/Publication: 2021-08-05 source.ver: src/contrib/rfaRm_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/rfaRm_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/rfaRm_1.4.3.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: 48 Package: Rfastp Version: 1.2.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 180c823697b47e6fde3adaaca870f61c 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_13 git_last_commit: c35bc0b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rfastp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rfastp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rfastp_1.2.0.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: 47 Package: rfPred Version: 1.30.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) MD5sum: c357f93b066edaf7d27cfe054bb761b5 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 be connected on the Internet or 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 URL: http://www.sbim.fr/rfPred git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_13 git_last_commit: 1289b9d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rfPred_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/rfPred_1.30.0.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: 30 Package: rGADEM Version: 2.40.0 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 MD5sum: 00d2cecbac2928f58586042f869b7204 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_13 git_last_commit: 3d0cf8b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rGADEM_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rGADEM_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rGADEM_2.40.0.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: 46 Package: RGalaxy Version: 1.36.0 Depends: XML, methods, tools, optparse Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: ede1a992a6e921881c2cd487352349ce NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGalaxy git_branch: RELEASE_3_13 git_last_commit: 0fa126d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RGalaxy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGalaxy_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGalaxy_1.36.0.tgz vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html vignetteTitles: Introduction to RGalaxy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R dependencyCount: 37 Package: Rgin Version: 1.12.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.5.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4f3a8e0f045f6b63d3d988f148b12ee8 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: RELEASE_3_13 git_last_commit: 71ce57c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rgin_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rgin_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rgin_1.12.0.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: 1.12.4 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: 9e913b52bd31aa4f16c7c3c749ca3e1d 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_13 git_last_commit: b816075 git_last_commit_date: 2021-07-19 Date/Publication: 2021-07-20 source.ver: src/contrib/RGMQL_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGMQL_1.12.4.zip 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: RGraph2js Version: 1.20.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 76681245f32e67085cc8c4488b5ef382 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_13 git_last_commit: 3a5e683 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RGraph2js_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGraph2js_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGraph2js_1.20.0.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: 11 Package: Rgraphviz Version: 2.36.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: 4c108dc2322a36b751bc9ecbfd79b715 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_13 git_last_commit: 1ea05ef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rgraphviz_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rgraphviz_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rgraphviz_2.36.0.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, abn, dlsem, geneNetBP, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CompGO, CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, MIGSA, mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, SplicingGraphs, trackViewer, TRONCO, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet, gRain, gRbase, gRim, hmma, hpoPlot, maGUI, MetaClean, ontologyPlot, SEMgraph, stablespec, wiseR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, 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, MCDA, msSurv, multiplex, ParallelPC, pcalg, psych, relations, rEMM, rPref, RSeed, SCCI, sisal, SourceSet, textplot, tm, topologyGSA, unifDAG, zenplots dependencyCount: 10 Package: rGREAT Version: 1.24.0 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 Archs: i386, x64 MD5sum: b556d14d66cb85e7b56e1685eaa08fff 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_13 git_last_commit: d3aad8c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rGREAT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rGREAT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rGREAT_1.24.0.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: 22 Package: RGSEA Version: 1.26.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 9d77540acb31e08246934f20314ed7d4 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_13 git_last_commit: 8c2b030 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RGSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGSEA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGSEA_1.26.0.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: 6 Package: rgsepd Version: 1.24.0 Depends: R (>= 4.0.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 Archs: i386, x64 MD5sum: 9c6d537dc167ad2f8a2afd13c4a7a689 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_13 git_last_commit: a30ee67 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rgsepd_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rgsepd_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rgsepd_1.24.0.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: 2.36.0 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2 License: Artistic-2.0 MD5sum: 05cfc23c6275c019e9aba5ba37325adc 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_13 git_last_commit: 4dc527f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rhdf5_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5_2.36.0.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: GenoGAM, GSCA, HDF5Array, HiCBricks, LoomExperiment importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HiCcompare, IONiseR, MOFA2, phantasus, ptairMS, PureCN, recountmethylation, ribor, scCB2, scone, signatureSearch, slinky, trackViewer, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.GRCh38, DmelSGI, MethylSeqData, ptairData, signatureSearchData, bioRad, file2meco, NEONiso, ondisc, smapr suggestsMe: edgeR, rhdf5filters, slalom, Spectra, SummarizedExperiment, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, digitalDLSorteR, hadron, io, isoreader, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX dependencyCount: 3 Package: rhdf5client Version: 1.14.2 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 15ca37c93e1c5b12bdbff7ba950d1268 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_13 git_last_commit: a24e7d8 git_last_commit_date: 2021-06-23 Date/Publication: 2021-06-24 source.ver: src/contrib/rhdf5client_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5client_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5client_1.14.2.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: 26 Package: rhdf5filters Version: 1.4.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), rhdf5 (>= 2.34.0) License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 14ad76dc3ecffedd8d273cb2b0836e6b 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_13 git_last_commit: c55c70e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rhdf5filters_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5filters_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5filters_1.4.0.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: 1.14.2 Depends: R (>= 4.0.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 MD5sum: 1778d6ec886c02ea2be4c33c824f4469 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_13 git_last_commit: 500fdf6 git_last_commit_date: 2021-07-05 Date/Publication: 2021-07-06 source.ver: src/contrib/Rhdf5lib_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhdf5lib_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhdf5lib_1.14.2.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: 1.8.0 Depends: R (>= 3.6) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 1ee04280788332000d59337b9e53a021 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_13 git_last_commit: d0f4299 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rhisat2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhisat2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhisat2_1.8.0.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: QuasR dependencyCount: 99 Package: Rhtslib Version: 1.24.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) MD5sum: 07b6e66499a3f97e30404b6950381249 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_13 git_last_commit: 28051cc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rhtslib_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhtslib_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhtslib_1.24.0.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, scPipe linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, h5vc, maftools, methylKit, podkat, qrqc, QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead, TransView, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboDiPA Version: 1.0.0 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 LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) Archs: i386, x64 MD5sum: 4e200b83d08d7a201a629d40d42e1665 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_13 git_last_commit: a39b378 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RiboDiPA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RiboDiPA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RiboDiPA_1.0.0.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: 149 Package: RiboProfiling Version: 1.22.0 Depends: R (>= 3.2.2), 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 Archs: i386, x64 MD5sum: 698e3608e00736a4587d3416ca957c90 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_13 git_last_commit: a315cdd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RiboProfiling_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RiboProfiling_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RiboProfiling_1.22.0.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: 157 Package: ribor Version: 1.4.0 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: i386, x64 MD5sum: 24898cae5c4bdc309055eb6ba2fa57d9 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_13 git_last_commit: ca9bd2a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ribor_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ribor_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ribor_1.4.0.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: 54 Package: riboSeqR Version: 1.26.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: f909b5445ef6129533f3b63966527990 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_13 git_last_commit: 8c2c4ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/riboSeqR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/riboSeqR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/riboSeqR_1.26.0.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: 38 Package: ribosomeProfilingQC Version: 1.4.0 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 Archs: x64 MD5sum: 5a53b4ef437403860a445b0a27d63806 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_13 git_last_commit: 482e0e7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ribosomeProfilingQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ribosomeProfilingQC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ribosomeProfilingQC_1.4.0.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: 137 Package: RImmPort Version: 1.20.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 85d863549a5ae08a7449e79716412daf 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_13 git_last_commit: 1565e15 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RImmPort_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RImmPort_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RImmPort_1.20.0.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: 43 Package: Ringo Version: 1.56.0 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 Archs: i386, x64 MD5sum: 6c8786653858874d0391bf885321ccb4 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_13 git_last_commit: 3da76e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Ringo_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Ringo_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Ringo_1.56.0.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: 83 Package: RIPAT Version: 1.2.0 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: 686e26b9b385bd45439e6786d9c943ae 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_13 git_last_commit: 1c308b1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RIPAT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RIPAT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RIPAT_1.2.0.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: 148 Package: Risa Version: 1.34.0 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 MD5sum: 180f218cd1f7ebe26f091723686a667b 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_13 git_last_commit: 681a687 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Risa_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Risa_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Risa_1.34.0.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: 98 Package: RITAN Version: 1.16.0 Depends: R (>= 3.4), Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata Suggests: rmarkdown License: file LICENSE MD5sum: af5bb336831a4ccfd4fb8b4e0b0c2e0b 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 Author: Michael Zimmermann Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_13 git_last_commit: ded8612 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RITAN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RITAN_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RITAN_1.16.0.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: 115 Package: RIVER Version: 1.16.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: b5a073c98aa199f46c2a11e6da49e6e0 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_13 git_last_commit: ed4a4d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RIVER_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RIVER_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RIVER_1.16.0.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: 50 Package: RJMCMCNucleosomes Version: 1.16.0 Depends: R (>= 3.4), 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: e704e632c1631b7deb9a52fc85ab2ca6 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_13 git_last_commit: ff26964 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RJMCMCNucleosomes_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RJMCMCNucleosomes_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RJMCMCNucleosomes_1.16.0.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: 50 Package: RLassoCox Version: 1.0.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: e4e9a5008bb9b8f8d22257b58da29424 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_13 git_last_commit: 81d0c12 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RLassoCox_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RLassoCox_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RLassoCox_1.0.0.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: 18 Package: RLMM Version: 1.54.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 6dc1480a1db043c92153a5c1b5ef4f0f 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_13 git_last_commit: 76954af git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RLMM_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RLMM_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RLMM_1.54.0.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: Rmagpie Version: 1.48.0 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: 16473f5d6dc5a716d3f8e62634a62337 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_13 git_last_commit: a465b4a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rmagpie_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rmagpie_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rmagpie_1.48.0.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: 20 Package: RMassBank Version: 3.2.0 Depends: Rcpp Imports: XML,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat Suggests: BiocStyle,gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, RUnit, knitr License: Artistic-2.0 MD5sum: e1240514bc1fa76e6d9f55e3e88e5743 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_13 git_last_commit: ba135d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RMassBank_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RMassBank_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RMassBank_3.2.0.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: 95 Package: rmelting Version: 1.8.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.5-0) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: 693b7fe40c244a229091f3e6ead566c9 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_13 git_last_commit: 856c547 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rmelting_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rmelting_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rmelting_1.8.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: RmiR Version: 1.48.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: c415005570b732cd9e7ccaf316c95b7e NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/RmiR git_branch: RELEASE_3_13 git_last_commit: 590c194 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RmiR_1.48.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RmiR_1.48.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R dependencyCount: 48 Package: Rmmquant Version: 1.10.0 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 Archs: i386, x64 MD5sum: 3272ce8255166240a2f064f777881f40 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_13 git_last_commit: 5bad2b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rmmquant_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rmmquant_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rmmquant_1.10.0.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: 167 Package: RNAAgeCalc Version: 1.4.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 557659d457e513be84a4367ad5ca4035 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_13 git_last_commit: a4fe63d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAAgeCalc_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAAgeCalc_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAAgeCalc_1.4.0.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: 161 Package: RNAdecay Version: 1.12.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: bdac324f4b2309641fb7496c3596acc1 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_13 git_last_commit: cf277c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAdecay_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RNAdecay_1.12.0.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: 47 Package: rnaEditr Version: 1.2.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: d6ebd73aeb8187b2e6f91cb5cb9bf83c 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_13 git_last_commit: 377f691 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rnaEditr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rnaEditr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rnaEditr_1.2.0.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: 118 Package: RNAinteract Version: 1.40.0 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 MD5sum: 097eca2bfde88621ff9518defcbd450e 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_13 git_last_commit: e2582be git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAinteract_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAinteract_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAinteract_1.40.0.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: 107 Package: RNAmodR Version: 1.6.0 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: a47b7616b5227f365b1a4a0507d04b1c 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_13 git_last_commit: c7b25bf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAmodR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR_1.6.0.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: 151 Package: RNAmodR.AlkAnilineSeq Version: 1.6.0 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: 4b42200276e241092fb35ead97f1d0e7 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_13 git_last_commit: cd821d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.AlkAnilineSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.AlkAnilineSeq_1.6.0.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: 152 Package: RNAmodR.ML Version: 1.6.0 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 Archs: i386, x64 MD5sum: 3d31450cd675cfa5c454825722fb288b 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_13 git_last_commit: 70709a5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAmodR.ML_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.ML_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.ML_1.6.0.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: 154 Package: RNAmodR.RiboMethSeq Version: 1.6.0 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: 1fbbbb124520af2c643317179191c9d3 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_13 git_last_commit: b1876ea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.RiboMethSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.RiboMethSeq_1.6.0.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: 152 Package: RNAsense Version: 1.6.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 7ec214cba04237e97edc1dc7410f0bbe 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_13 git_last_commit: d486415 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNAsense_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAsense_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAsense_1.6.0.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: 63 Package: rnaseqcomp Version: 1.22.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 25c9be05e53840d535dd2fcb7052d5eb 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_13 git_last_commit: 53163e9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rnaseqcomp_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rnaseqcomp_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rnaseqcomp_1.22.0.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: 1.32.0 License: LGPL (>=2) MD5sum: 823dc610c7cf53fb5054e4ca7b3bc3b2 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_13 git_last_commit: aab663a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNASeqPower_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNASeqPower_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNASeqPower_1.32.0.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 importsMe: DGEobj.utils dependencyCount: 0 Package: RNASeqR Version: 1.10.0 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, png, grid, RNASeqRData License: Artistic-2.0 MD5sum: 8b67f30235b26eb354499f4a852e75c6 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 git_url: https://git.bioconductor.org/packages/RNASeqR git_branch: RELEASE_3_13 git_last_commit: c89d5c1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RNASeqR_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RNASeqR_1.10.0.tgz vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html vignetteTitles: RNA-Seq analysis based on one independent variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R dependencyCount: 269 Package: RnaSeqSampleSize Version: 2.2.0 Depends: R (>= 4.0.0), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, utils,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: a783d469c60bc8d94d2f90c044322b33 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 Developer [aut], Yan Guo Developer [aut], Quanhu Sheng Developer [aut], Yu Shyr Developer [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_13 git_last_commit: 88b5b96 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RnaSeqSampleSize_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RnaSeqSampleSize_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RnaSeqSampleSize_2.2.0.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: 81 Package: RnBeads Version: 2.10.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, 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: dc4cdda2c94dd19e817d2ba788505264 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_13 git_last_commit: f6ec71a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RnBeads_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RnBeads_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RnBeads_2.10.0.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: 166 Package: Rnits Version: 1.26.0 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 MD5sum: c8228028dba9209474344b12ecd76789 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: RELEASE_3_13 git_last_commit: 756cbae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rnits_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rnits_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rnits_1.26.0.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: 56 Package: roar Version: 1.28.0 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: d2c43f6a4e6db4c2ff20c2a84ab52ce3 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_13 git_last_commit: 34c7fa7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/roar_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/roar_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/roar_1.28.0.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: 44 Package: ROC Version: 1.68.1 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase License: Artistic-2.0 MD5sum: 33bd40ed90664477d759991d5dd0e0c1 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_13 git_last_commit: 2c48100 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/ROC_1.68.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROC_1.68.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ROC_1.68.1.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: 1.4.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: ddbb9e8df20b649a8fd975de94832dc1 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_13 git_last_commit: 137d7ce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ROCpAI_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROCpAI_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROCpAI_1.4.0.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: 37 Package: rols Version: 2.20.1 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 Archs: i386, x64 MD5sum: 154d463ba878e933eda6e9a993d92528 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_13 git_last_commit: 2148a4e git_last_commit_date: 2021-06-15 Date/Publication: 2021-06-15 source.ver: src/contrib/rols_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rols_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rols_2.20.1.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 dependsOnMe: proteomics importsMe: spatialHeatmap suggestsMe: MSnbase, RforProteomics dependencyCount: 27 Package: ROntoTools Version: 2.20.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 15cad42fc80ae42690d7e37596c6eb44 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_13 git_last_commit: b1691c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ROntoTools_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROntoTools_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROntoTools_2.20.0.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: 35 Package: ropls Version: 1.24.0 Depends: Biobase Imports: graphics, grDevices, methods, MultiDataSet, stats Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 5addfe868974e8544677ee26e1420255 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 Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_13 git_last_commit: 9442690 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ropls_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ropls_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ropls_1.24.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R dependsOnMe: biosigner importsMe: ASICS, lipidr, MultiBaC, proFIA, MetabolomicsBasics suggestsMe: autonomics, ptairMS, structToolbox dependencyCount: 62 Package: ROSeq Version: 1.4.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 91040e48e12b28cd1f49b37dd40441bf 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_13 git_last_commit: 78c7da3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ROSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROSeq_1.4.0.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: 1.20.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: b9ac0318c3a12a049d768a178e1a0bb3 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_13 git_last_commit: d24bde8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ROTS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROTS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROTS_1.20.0.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 suggestsMe: wrProteo dependencyCount: 8 Package: RPA Version: 1.48.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: dd977c85f0fc792b0375e029f2e74c86 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_13 git_last_commit: be1119f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RPA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RPA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RPA_1.48.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 81 Package: RProtoBufLib Version: 2.4.0 License: BSD_3_clause MD5sum: 3857ba944035b429c6b029076b98e021 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_13 git_last_commit: 49aa129 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RProtoBufLib_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RProtoBufLib_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RProtoBufLib_2.4.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: RpsiXML Version: 2.34.0 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: 17b0f2a3a6500cc987a1b7af9488b7c6 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 git_url: https://git.bioconductor.org/packages/RpsiXML git_branch: RELEASE_3_13 git_last_commit: 94db0a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RpsiXML_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RpsiXML_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RpsiXML_2.34.0.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 dependsOnMe: ScISI importsMe: ScISI dependencyCount: 53 Package: rpx Version: 2.0.3 Depends: methods Imports: BiocFileCache, jsonlite, xml2, RCurl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, rmarkdown License: GPL-2 MD5sum: d8ba4fd736403469af562c4278ba60fa 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_13 git_last_commit: 9d84fbe git_last_commit_date: 2021-08-17 Date/Publication: 2021-08-17 source.ver: src/contrib/rpx_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/rpx_2.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/rpx_2.0.3.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 dependsOnMe: proteomics importsMe: MBQN suggestsMe: MSnbase, RforProteomics dependencyCount: 50 Package: Rqc Version: 1.26.0 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: testthat License: GPL (>= 2) MD5sum: 845fda7a4202781771c53b7dc79259b6 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_13 git_last_commit: 1c1c91a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rqc_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rqc_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rqc_1.26.0.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: 164 Package: rqt Version: 1.18.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: bedd8b92012ebd56a3a14eee6f2d7884 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_13 git_last_commit: bcd7dae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rqt_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rqt_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rqt_1.18.0.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: 141 Package: rqubic Version: 1.38.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 8801b9993cab1e9f9e5a0d303b10a476 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_13 git_last_commit: 8d90216 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rqubic_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rqubic_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rqubic_1.38.0.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: 53 Package: rRDP Version: 1.26.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE Archs: i386, x64 MD5sum: 604e10f8d22a640176d0893549460ffc 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_13 git_last_commit: 2a5507e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rRDP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rRDP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rRDP_1.26.0.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: 19 Package: RRHO Version: 1.32.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 3c89e53ddcb6feed739a2b78c245b208 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_13 git_last_commit: 589c56d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RRHO_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RRHO_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RRHO_1.32.0.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: 7 Package: rrvgo Version: 1.4.4 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.Pf.plasmo.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: 6775da27b493200fff5d6a829591b5d6 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_13 git_last_commit: 60a1114 git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/rrvgo_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/rrvgo_1.4.4.zip mac.binary.ver: bin/macosx/contrib/4.1/rrvgo_1.4.4.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 dependencyCount: 101 Package: Rsamtools Version: 2.8.0 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 Archs: i386, x64 MD5sum: 804ede99db45d6eb2b8b72da107bf016 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_13 git_last_commit: 45d46ab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rsamtools_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rsamtools_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rsamtools_2.8.0.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, rfPred, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook, Brundle importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics, BSgenome, CAGEr, casper, cellbaseR, 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, epialleleR, epigenomix, epigraHMM, eudysbiome, FilterFFPE, FunChIP, gcapc, GeneGeneInteR, GenoGAM, 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, recoup, Repitools, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc, rtracklayer, scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, chipseqDBData, LungCancerLines, MMAPPR2data, systemPipeRdata, BinQuasi, ExomeDepth, hoardeR, intePareto, kibior, MAAPER, MicroSEC, NIPTeR, noisyr, PlasmaMutationDetector, pulseTD, RAPIDR, Signac, spp, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, IRanges, omicsPrint, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, chipseqDB, polyRAD, seqmagick dependencyCount: 28 Package: rsbml Version: 2.50.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: a2a2dec3103708182f9291252ddb2079 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_13 git_last_commit: 84b2f61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rsbml_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rsbml_2.50.0.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: 8 Package: rScudo Version: 1.8.0 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: i386, x64 MD5sum: 91d707d01c714e3cacb31c7440e36ed9 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_13 git_last_commit: 673d670 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rScudo_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rScudo_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rScudo_1.8.0.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: 1.2.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 8e43f99e8300d66c413de9b5630693b5 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_13 git_last_commit: 786285d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rsemmed_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rsemmed_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rsemmed_1.2.0.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: 30 Package: RSeqAn Version: 1.12.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 9e98137e5d831265830b7e2386e33243 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_13 git_last_commit: c59a5d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RSeqAn_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RSeqAn_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RSeqAn_1.12.0.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: 2.6.4 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: 8c60914f66d294da4ab72053466c91c5 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_13 git_last_commit: 77deb77 git_last_commit_date: 2021-07-14 Date/Publication: 2021-07-15 source.ver: src/contrib/Rsubread_2.6.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rsubread_2.6.4.zip mac.binary.ver: bin/macosx/contrib/4.1/Rsubread_2.6.4.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, SEAA suggestsMe: autonomics, icetea, scPipe, singleCellTK, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.32.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 Archs: i386, x64 MD5sum: d4ebf308413b7594f932a4d340b12bb8 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_13 git_last_commit: 7c52c69 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RSVSim_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RSVSim_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RSVSim_1.32.0.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: 44 Package: rSWeeP Version: 1.4.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: db04a8b079c4f4d16918a92ffab270f3 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_13 git_last_commit: e8a62ab git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rSWeeP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rSWeeP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rSWeeP_1.4.0.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: 1.44.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: e3d2dbe9c371296fb7111dd8f0f7ca53 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_13 git_last_commit: 0e775e9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTCA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCA_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCA_1.44.0.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTCGA Version: 1.22.0 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, 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: d1678bd83ffaddf7ae3d37e48530e585 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_13 git_last_commit: 98a46dc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTCGA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCGA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCGA_1.22.0.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: 134 Package: RTCGAToolbox Version: 2.22.1 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: 350d89cfdb2302f4058bb4c5c381d978 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_13 git_last_commit: adc18d9 git_last_commit_date: 2021-06-14 Date/Publication: 2021-06-15 source.ver: src/contrib/RTCGAToolbox_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCGAToolbox_2.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCGAToolbox_2.22.1.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: 114 Package: RTN Version: 2.16.0 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 Archs: i386, x64 MD5sum: 6b560e9070f77e9b60aa4ef1b3d85395 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_13 git_last_commit: ae869f1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTN_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTN_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTN_2.16.0.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: 124 Package: RTNduals Version: 1.16.0 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: 4e44633e931651d7bc7ac2c99e0a87a0 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_13 git_last_commit: b30aac2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTNduals_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTNduals_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTNduals_1.16.0.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: 125 Package: RTNsurvival Version: 1.16.0 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: e21b24efa66b7efbd53a7bbb6bc41d32 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_13 git_last_commit: 981ddae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTNsurvival_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTNsurvival_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTNsurvival_1.16.0.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: 132 Package: RTopper Version: 1.38.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 6e9af6b8c5830b6f523d99d3da5882f2 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_13 git_last_commit: 1c351a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RTopper_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTopper_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTopper_1.38.0.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: 17 Package: Rtpca Version: 1.2.0 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: 507b686e12027997cfb795e1bf50ce04 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_13 git_last_commit: acf0a22 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rtpca_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rtpca_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rtpca_1.2.0.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: 51 Package: rtracklayer Version: 1.52.1 Depends: R (>= 3.3), 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: 9ed518471b5dc09acd93be49bcd7fe42 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_13 git_last_commit: 20a3831 git_last_commit_date: 2021-08-12 Date/Publication: 2021-08-15 source.ver: src/contrib/rtracklayer_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rtracklayer_1.52.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rtracklayer_1.52.1.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, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro, HiCfeat importsMe: ALPS, AnnotationHubData, annotatr, APAlyzer, ASpediaFI, ATACseqQC, ballgown, BgeeCall, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, chromswitch, circRNAprofiler, CNEr, coMET, compartmap, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, DeepBlueR, derfinder, DEScan2, diffHic, diffloop, diffUTR, DMCFB, DMCHMM, dmrseq, ELMER, enrichTF, ensembldb, epidecodeR, epigraHMM, erma, esATAC, fcScan, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, gmapR, gmoviz, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, MACPET, MADSEQ, maser, MEDIPS, metagene, metagene2, metaseqR2, methrix, methyAnalysis, methylKit, motifbreakR, MotifDb, multicrispr, NADfinder, nanotatoR, nearBynding, normr, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, ribosomeProfilingQC, RIPAT, RNAmodR, roar, scPipe, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, SigsPack, sitadela, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, tRNAscanImport, TSRchitect, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, systemPipeRdata, SingscoreAMLMutations, crispRdesignR, GALLO, kibior, PlasmaMutationDetector, utr.annotation suggestsMe: alpine, AnnotationHub, autonomics, BiocFileCache, biovizBase, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, DAMEfinder, dasper, eisaR, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicRanges, goseq, gwascat, InPAS, interactiveDisplay, megadepth, methylumi, miRBaseConverter, MutationalPatterns, OrganismDbi, Pi, PICS, PING, pipeFrame, pqsfinder, R453Plus1Toolbox, RcisTarget, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, signeR, similaRpeak, SynExtend, TAPseq, TCGAutils, triplex, tRNAdbImport, TVTB, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, FDb.FANTOM4.promoters.hg19, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, spatialLIBD, chipseqDB, gkmSVM, LDheatmap, RTIGER, Seurat, Signac dependencyCount: 43 Package: Rtreemix Version: 1.54.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: b6f182c241cdd5a1437959e31e9eed26 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_13 git_last_commit: 5821181 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Rtreemix_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rtreemix_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rtreemix_1.54.0.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: 73 Package: rTRM Version: 1.30.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: c359ccfe2da7b349aed33690fdf1dce0 NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI 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_13 git_last_commit: 4d427e2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rTRM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rTRM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rTRM_1.30.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui dependencyCount: 51 Package: rTRMui Version: 1.30.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 6027308b3166ecff26f61e845d3c6410 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_13 git_last_commit: d5e4738 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/rTRMui_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rTRMui_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rTRMui_1.30.0.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: 96 Package: runibic Version: 1.14.0 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: abd73d123157f9285601c441d88d3b53 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_13 git_last_commit: 71ead1e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/runibic_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/runibic_1.14.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 83 Package: RUVcorr Version: 1.24.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db License: GPL-2 MD5sum: 9aef327980f82d51b0ac0939ee2b33f9 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_13 git_last_commit: 2673612 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RUVcorr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVcorr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVcorr_1.24.0.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: 1.26.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 Archs: i386, x64 MD5sum: 25d4f664fdb539a7f28bacd5d2d55aff 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_13 git_last_commit: b11bbc7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RUVnormalize_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVnormalize_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVnormalize_1.26.0.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: 8 Package: RUVSeq Version: 1.26.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 3f44498d8b275c806e011f62ab6591f9 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_13 git_last_commit: b6d90ae git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RUVSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVSeq_1.26.0.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: 111 Package: RVS Version: 1.14.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 941bf58b0376cc66796fe480cc487a01 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_13 git_last_commit: 02ba79e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/RVS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RVS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RVS_1.14.0.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: 35 Package: rWikiPathways Version: 1.12.0 Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7dde9ce483c83f5451681526d9989581 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_13 git_last_commit: 1b68185 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/rWikiPathways_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rWikiPathways_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rWikiPathways_1.12.0.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: 36 Package: S4Vectors Version: 0.30.2 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 Archs: i386, x64 MD5sum: 7f6216bf0994150690c6dbbe9202eb57 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_13 git_last_commit: 87b7827 git_last_commit_date: 2021-09-30 Date/Publication: 2021-10-03 source.ver: src/contrib/S4Vectors_0.30.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/S4Vectors_0.30.2.zip mac.binary.ver: bin/macosx/contrib/4.1/S4Vectors_0.30.2.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: 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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, FlowSorted.Blood.EPIC, generegulation, pagoo importsMe: ADImpute, affycoretools, aggregateBioVar, airpart, ALDEx2, AllelicImbalance, alpine, AlpsNMR, amplican, AneuFinder, animalcules, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, appreci8R, ASpediaFI, ASpli, AUCell, autonomics, BadRegionFinder, ballgown, barcodetrackR, BASiCS, batchelor, BayesSpace, 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DMRcate, dmrseq, doseR, DRIMSeq, DropletUtils, drugTargetInteractions, easyRNASeq, eegc, eisaR, ELMER, EnrichmentBrowser, enrichTF, ensembldb, ensemblVEP, epigraHMM, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, ExperimentHub, ExperimentSubset, ExploreModelMatrix, FastqCleaner, fastseg, FilterFFPE, FindMyFriends, fishpond, flowCore, flowWorkspace, FRASER, GA4GHshiny, gcapc, GDSArray, genbankr, GeneRegionScan, GENESIS, GeneTonic, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicSuperSignature, GeomxTools, ggbio, Glimma, gmapR, gmoviz, GOpro, GOTHiC, GRmetrics, GSEABenchmarkeR, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, HiCBricks, HiCcompare, HiLDA, hipathia, hmdbQuery, HTSeqGenie, HumanTranscriptomeCompendium, icetea, ideal, ILoReg, IMAS, INSPEcT, InteractionSet, InteractiveComplexHeatmap, InterMineR, iSEE, iSEEu, isomiRs, IVAS, ivygapSE, karyoploteR, kebabs, lionessR, lipidr, lisaClust, loci2path, LOLA, MACPET, MACSr, MADSEQ, marr, MAST, MatrixQCvis, mbkmeans, mCSEA, MEAL, meshr, MesKit, metabCombiner, metaseqR2, MetCirc, MethCP, methInheritSim, MethReg, methylCC, methylInheritance, methylKit, methylPipe, methylSig, methylumi, mia, miaViz, midasHLA, miloR, mimager, minfi, MinimumDistance, MIRA, MiRaGE, missMethyl, missRows, MMAPPR2, MMDiff2, moanin, Modstrings, mosaics, MOSim, motifbreakR, motifmatchr, mpra, msa, MsBackendMassbank, MsBackendMgf, MsCoreUtils, msgbsR, MSPrep, MultiAssayExperiment, MultiDataSet, mumosa, muscat, musicatk, MutationalPatterns, mygene, myvariant, NanoMethViz, ncRNAtools, nearBynding, nucleoSim, nucleR, oligoClasses, 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, pulsedSilac, PureCN, PWMEnrich, qcmetrics, QFeatures, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, RcwlPipelines, recount, recount3, recountmethylation, recoup, regioneR, regionReport, regsplice, regutools, REMP, Repitools, ResidualMatrix, restfulSE, rexposome, rfaRm, RGMQL, rhdf5client, RiboDiPA, RiboProfiling, ribor, ribosomeProfilingQC, RJMCMCNucleosomes, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, Rqc, Rsamtools, rScudo, RTCGAToolbox, RTN, rtracklayer, SC3, ScaledMatrix, SCArray, scater, scClassify, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, SCOPE, scp, scPipe, scran, scruff, scTensor, scTGIF, scuttle, sechm, SeqArray, seqCAT, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead, SingleCellExperiment, singleCellTK, SingleR, singscore, sitadela, skewr, slingshot, SMITE, SNPhood, soGGi, SomaticSignatures, Spaniel, SpatialExperiment, spatialHeatmap, spicyR, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff, STAN, strandCheckR, struct, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, systemPipeR, TAPseq, TarSeqQC, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ, tidySingleCellExperiment, tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq, TrajectoryUtils, transcriptR, TransView, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TSRchitect, TVTB, twoddpcr, tximeta, Ularcirc, UMI4Cats, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, VaSP, VCFArray, velociraptor, VplotR, wavClusteR, weitrix, wiggleplotr, XCIR, xcms, 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.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, MafH5.gnomAD.v3.1.1.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, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DropletTestFiles, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, imcdatasets, leeBamViews, MetaGxPancreas, MethylSeqData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scpdata, scRNAseq, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS, BinQuasi, crispRdesignR, digitalDLSorteR, driveR, genBaRcode, geno2proteo, hoardeR, imcExperiment, LoopRig, microbial, NIPTeR, oncoPredict, PlasmaMutationDetector, pulseTD, restfulr, rsolr, SC.MEB, Signac suggestsMe: BiocGenerics, conclus, dearseq, epivizrChart, globalSeq, GWASTools, GWENA, maftools, martini, MicrobiotaProcess, MungeSumstats, RTCGA, TFEA.ChIP, TFutils, tidybulk, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, cancerTiming, GeoTcgaData, gkmSVM, polyRAD, rliger, Seurat, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: safe Version: 3.32.0 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: c4234f9a536190ca17b236f9a49f1eed 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_13 git_last_commit: 0919b3a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/safe_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/safe_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/safe_3.32.0.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: 47 Package: sagenhaft Version: 1.62.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: 9d32ad1afefafe45883a555c875b699a 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_13 git_last_commit: 202f5f0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sagenhaft_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sagenhaft_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sagenhaft_1.62.0.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: 1.6.0 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 License: GPL-3 MD5sum: d27b0b17e0e084f536d1dc1546924d1f 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 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_13 git_last_commit: bbfe3bd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SAIGEgds_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SAIGEgds_1.5.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SAIGEgds_1.6.0.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: 1.16.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: 602ce94c491830329450300e397090f6 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_13 git_last_commit: 2f67267 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sampleClassifier_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sampleClassifier_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sampleClassifier_1.16.0.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: 96 Package: SamSPECTRAL Version: 1.46.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: fe0a4da1747f344a41cd66de3ed9eaab 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_13 git_last_commit: 89f0f33 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SamSPECTRAL_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SamSPECTRAL_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SamSPECTRAL_1.46.0.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: 1.2.0 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, kableExtra, seqinr, BiocStyle, logger Suggests: knitr, testthat (>= 2.1.0) License: GPL-2 Archs: i386, x64 MD5sum: 211e06e9893d60dc7bc1549975938d7d 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_13 git_last_commit: 1dc9873 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sangeranalyseR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sangeranalyseR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sangeranalyseR_1.2.0.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: 143 Package: sangerseqR Version: 1.28.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 9012cee809f4f5c86766e2aba477be5c 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_13 git_last_commit: cdb10aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sangerseqR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sangerseqR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sangerseqR_1.28.0.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: 47 Package: SANTA Version: 2.28.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: RUnit, BiocGenerics, knitr, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) MD5sum: ab3171d3a6cd965007b1243ff6b5b63a 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_13 git_last_commit: 574349d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SANTA_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SANTA_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SANTA_2.28.0.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: 11 Package: sarks Version: 1.4.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 06edd5d7bd169c7134266a62c5f06a70 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_13 git_last_commit: 47f8e9b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sarks_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sarks_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sarks_1.4.0.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: 22 Package: satuRn Version: 1.0.0 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 MD5sum: d1f906084636cce8a0591e92072871d8 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_13 git_last_commit: d3fe0b2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/satuRn_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/satuRn_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/satuRn_1.0.0.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: 67 Package: savR Version: 1.30.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 Archs: i386, x64 MD5sum: 1c4e64ccd32332f676cce28df324a293 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_13 git_last_commit: 2e0da91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/savR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/savR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/savR_1.30.0.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: 46 Package: SBGNview Version: 1.6.0 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: 9cf5eef8e82aa1c2e96cf4c2a6041f2b 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_13 git_last_commit: 7fc4f04 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SBGNview_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SBGNview_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SBGNview_1.6.0.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: 81 Package: SBMLR Version: 1.88.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: ff7ae234895c5a9acb744ab453342f8a 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_13 git_last_commit: e247ba0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SBMLR_1.88.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SBMLR_1.88.0.zip 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: 1.20.0 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 Archs: i386, x64 MD5sum: 468b95a01770bc43e20cbdd3a34cc52f 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_13 git_last_commit: 2fc947c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SC3_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SC3_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SC3_1.20.0.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 dependencyCount: 100 Package: Scale4C Version: 1.14.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 8a2a06a4a228e6db3a08cff9a703fda2 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_13 git_last_commit: c276a31 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Scale4C_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Scale4C_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Scale4C_1.14.0.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: 27 Package: ScaledMatrix Version: 1.0.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: ebde9157e2153e388c704a3fcf837dd4 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_13 git_last_commit: 84cb9ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ScaledMatrix_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ScaledMatrix_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ScaledMatrix_1.0.0.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: 16 Package: scAlign Version: 1.6.0 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: f22bab87829351b77dcc701be7454cd9 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: RELEASE_3_13 git_last_commit: 8ef122b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scAlign_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scAlign_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scAlign_1.6.0.tgz 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: 165 Package: SCAN.UPC Version: 2.34.0 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: 5d2e6f1d56006e1506d44cce5c1bb10b 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_13 git_last_commit: 03c0797 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCAN.UPC_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCAN.UPC_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCAN.UPC_2.34.0.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: 108 Package: SCANVIS Version: 1.6.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: df370188ad88be90e3ad81a51810d65f 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_13 git_last_commit: 17a205b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCANVIS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCANVIS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCANVIS_1.6.0.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: 45 Package: SCArray Version: 1.0.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.27.4), methods, DelayedArray Imports: BiocGenerics, S4Vectors, IRanges, utils, SummarizedExperiment, SingleCellExperiment Suggests: Matrix, DelayedMatrixStats, scater, uwot, RUnit, knitr, markdown, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 3e2aae1500df295c9f7ab32f0d132353 NeedsCompilation: no 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_13 git_last_commit: 0faf080 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCArray_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCArray_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCArray_1.0.0.tgz vignettes: vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: 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: 28 Package: SCATE Version: 1.2.0 Depends: parallel, preprocessCore, splines, splines2, xgboost, SCATEData, Rtsne, mclust Imports: utils, stats, GenomicAlignments, GenomicRanges Suggests: rmarkdown, ggplot2, knitr License: MIT + file LICENSE MD5sum: d7f1a1de6fd7ed20b04eee2438f298fb 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_13 git_last_commit: f1f5a43 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCATE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCATE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCATE_1.2.0.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: 116 Package: scater Version: 1.20.1 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 Suggests: BiocStyle, biomaRt, cowplot, destiny, knitr, scRNAseq, robustbase, rmarkdown, uwot, NMF, testthat, pheatmap, snifter, Biobase License: GPL-3 MD5sum: 0ebf286f6adb03eef17024a4de3af76a 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_13 git_last_commit: 67e2515 git_last_commit_date: 2021-05-24 Date/Publication: 2021-06-15 source.ver: src/contrib/scater_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scater_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scater_1.20.1.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, IRISFGM, mia, miaViz, muscat, netDx, peco, pipeComp, scDblFinder, scPipe, singleCellTK, Spaniel, splatter, tricycle, spatialLIBD, SC.MEB suggestsMe: batchelor, bluster, CellaRepertorium, CellTrails, CiteFuse, dittoSeq, ExperimentSubset, fcoex, InteractiveComplexHeatmap, iSEE, iSEEu, M3Drop, MAST, mbkmeans, miloR, miQC, monocle, mumosa, Nebulosa, SC3, SCArray, scds, schex, scHOT, scMerge, scone, scp, scran, scRepertoire, SingleR, slalom, snifter, SummarizedBenchmark, tidySingleCellExperiment, velociraptor, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, muscData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, SingleRBook, bcTSNE dependencyCount: 81 Package: scBFA Version: 1.6.0 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: e6033ef482f6bc377d88ea907a6a6dc3 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_13 git_last_commit: 7c8cbac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scBFA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scBFA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scBFA_1.6.0.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: 190 Package: SCBN Version: 1.10.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 7c1632832efb8cb66d02267e1b3191d7 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_13 git_last_commit: 772ef87 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCBN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCBN_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCBN_1.10.0.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 dependencyCount: 1 Package: scCB2 Version: 1.2.0 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: 622d6e39e455e2a1d058df2ae078574e 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_13 git_last_commit: 6a7ccb6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scCB2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scCB2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scCB2_1.2.0.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: 180 Package: scClassifR Version: 1.0.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, utils Suggests: knitr, scRNAseq, testthat License: MIT + file LICENSE MD5sum: 41f0161d0a18d7c1aed2eff1ff9e5469 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. scClassifR 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scClassifR git_branch: RELEASE_3_13 git_last_commit: 2be48cc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scClassifR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scClassifR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scClassifR_1.0.0.tgz vignettes: vignettes/scClassifR/inst/doc/classifying-cells.html, vignettes/scClassifR/inst/doc/training-basic-model.html, vignettes/scClassifR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scClassifR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scClassifR/inst/doc/classifying-cells.R, vignettes/scClassifR/inst/doc/training-basic-model.R, vignettes/scClassifR/inst/doc/training-child-model.R dependencyCount: 178 Package: scClassify Version: 1.4.0 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 Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 50f7bc00f4f0a12587fd6a46c4543090 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_13 git_last_commit: 11b64e3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scClassify_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scClassify_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scClassify_1.4.0.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: 83 Package: scDataviz Version: 1.2.0 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: 590db66adf9c047bffbcc5a8120a6e0b 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_13 git_last_commit: 0b82f98 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scDataviz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDataviz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDataviz_1.2.0.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: 163 Package: scDblFinder Version: 1.6.0 Depends: R (>= 4.1) Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, SingleCellExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, mbkmeans Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, MASS, viridisLite License: GPL-3 MD5sum: 2e855dc2b7ae7ad798d8524ba1799624 NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell RNA sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, and the new fast and comprehensive scDblFinder method. biocViews: Preprocessing, SingleCell, RNASeq 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_13 git_last_commit: e5c1a83 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scDblFinder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDblFinder_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDblFinder_1.6.0.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/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 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/scDblFinder.R dependsOnMe: OSCA.advanced importsMe: singleCellTK dependencyCount: 119 Package: scDD Version: 1.16.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 68fc22a6d4eac35af3dfd4dd10241857 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_13 git_last_commit: 05c6b7b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scDD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDD_1.16.0.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: 123 Package: scde Version: 2.20.0 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: 95a128d4b0dd851fd367988088c649e8 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_13 git_last_commit: d19d2a0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scde_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scde_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scde_2.20.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 47 Package: scds Version: 1.8.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot License: MIT + file LICENSE MD5sum: 60e2437f8b806f35a64f19cfc4a4bf34 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_13 git_last_commit: 844eec6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scds_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scds_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scds_1.8.0.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: 51 Package: SCFA Version: 1.2.0 Depends: R (>= 4.0) Imports: matrixStats, keras, tensorflow, BiocParallel, igraph, Matrix, cluster, clusterCrit, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr License: LGPL MD5sum: b3441c0ae2d04fc4db37404e3d9ad17d 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_13 git_last_commit: 9619dda git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCFA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCFA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCFA_1.2.0.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: 63 Package: scFeatureFilter Version: 1.12.0 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: abff913301bcd3c3fc76d3ee00dba659 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_13 git_last_commit: 5b723a0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scFeatureFilter_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scFeatureFilter_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scFeatureFilter_1.12.0.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: 42 Package: scGPS Version: 1.6.0 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: c78da3ac75f576b67b47bff580eb72cf 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_13 git_last_commit: 503aaf0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scGPS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scGPS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scGPS_1.6.0.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: 137 Package: schex Version: 1.6.3 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: ae53ffc0722a9ba087638ddfc3954d07 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_13 git_last_commit: 4c45aec git_last_commit_date: 2021-06-06 Date/Publication: 2021-06-06 source.ver: src/contrib/schex_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/schex_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.1/schex_1.6.3.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: 171 Package: scHOT Version: 1.4.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 Archs: i386, x64 MD5sum: f1bfdf8dc88c71c36d04549683178b47 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_13 git_last_commit: 55a2ef4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scHOT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scHOT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scHOT_1.4.0.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: ScISI Version: 1.64.0 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 MD5sum: 7e978cc955282913f10616dc8bffab0e NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang git_url: https://git.bioconductor.org/packages/ScISI git_branch: RELEASE_3_13 git_last_commit: 1ee59b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ScISI_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ScISI_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ScISI_1.64.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: ppiStats, SLGI importsMe: SLGI suggestsMe: RpsiXML dependencyCount: 59 Package: scMAGeCK Version: 1.4.0 Imports: Seurat, stats, utils Suggests: knitr, rmarkdown License: BSD_2_clause MD5sum: 9cd3fdd39c41b289d598c2cd1a0d8ade 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 Maintainer: Xiaolong Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scMAGeCK git_branch: RELEASE_3_13 git_last_commit: c09bcb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scMAGeCK_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scMAGeCK_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scMAGeCK_1.4.0.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: 141 Package: scmap Version: 1.14.0 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: 8e1c116cdbd89fc9cc26b8c733d70577 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_13 git_last_commit: e59738b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scmap_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scmap_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scmap_1.14.0.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: 73 Package: scMerge Version: 1.8.0 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 Archs: i386, x64 MD5sum: 8ebd646fddb169fe46d19b178f32f53b 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_13 git_last_commit: b558998 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scMerge_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scMerge_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scMerge_1.8.0.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 dependencyCount: 119 Package: scmeth Version: 1.12.0 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 MD5sum: 8324c4f5e8fea3cfd9500e04572c2a05 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_13 git_last_commit: 1ad2a18 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scmeth_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scmeth_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scmeth_1.12.0.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: 164 Package: SCnorm Version: 1.14.0 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: defac70a40da768a842e11fe58718322 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_13 git_last_commit: b6a7f61 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCnorm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCnorm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCnorm_1.14.0.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: 74 Package: scone Version: 1.16.1 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: 94c09080148101f98b2db581c07957d9 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_13 git_last_commit: e028b2e git_last_commit_date: 2021-07-21 Date/Publication: 2021-07-22 source.ver: src/contrib/scone_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scone_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scone_1.16.1.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: 145 Package: Sconify Version: 1.12.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: acca6b2284760c68abe5bbe3e9e22cf9 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_13 git_last_commit: 68c5316 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Sconify_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Sconify_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Sconify_1.12.0.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: 68 Package: SCOPE Version: 1.4.0 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 Archs: i386, x64 MD5sum: dd464cf8efc725ca60996c1b7551f0c2 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_13 git_last_commit: 16e6b55 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SCOPE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCOPE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCOPE_1.4.0.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: 72 Package: scoreInvHap Version: 1.14.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 97a4e44f2d9b0fd3621d400bda5d5bc9 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_13 git_last_commit: 484fe25 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scoreInvHap_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scoreInvHap_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scoreInvHap_1.14.0.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: 101 Package: scp Version: 1.2.0 Depends: R (>= 4.0), QFeatures Imports: methods, stats, utils, SingleCellExperiment, SummarizedExperiment, MultiAssayExperiment, MsCoreUtils, matrixStats, S4Vectors, dplyr, magrittr, rlang Suggests: testthat, knitr, BiocStyle, BiocCheck, rmarkdown, patchwork, ggplot2, impute, scater, sva, preprocessCore, vsn, uwot License: Artistic-2.0 MD5sum: ce40d2b7defc237d08a75d7e7440b408 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_13 git_last_commit: a7c883a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scp_1.2.0.tgz vignettes: vignettes/scp/inst/doc/scp.html vignetteTitles: Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 57 Package: scPCA Version: 1.6.2 Depends: R (>= 4.0.2) 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: cbb8f818a16a60ed343937b6cc016853 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_13 git_last_commit: 2f31700 git_last_commit_date: 2021-05-26 Date/Publication: 2021-05-27 source.ver: src/contrib/scPCA_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/scPCA_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.1/scPCA_1.6.2.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: 68 Package: scPipe Version: 1.14.0 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: 7c177e26437e7a8576024b5227bf90bd 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_13 git_last_commit: 998ffca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scPipe_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scPipe_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scPipe_1.14.0.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: 153 Package: scran Version: 1.20.1 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: 435a34857a13ee9a6594cfe2efca28bf 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_13 git_last_commit: 5fcaf5b git_last_commit_date: 2021-05-24 Date/Publication: 2021-05-24 source.ver: src/contrib/scran_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scran_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scran_1.20.1.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, celda, ChromSCape, CiteFuse, conclus, IRISFGM, msImpute, mumosa, pipeComp, scDblFinder, scDD, SingleCellSignalR, singleCellTK, Spaniel, SC.MEB suggestsMe: batchelor, bluster, CellTrails, clusterExperiment, dittoSeq, ExperimentSubset, fcoex, glmGamPoi, iSEEu, miloR, Nebulosa, PCAtools, schex, scone, scuttle, SingleR, snifter, splatter, tidySingleCellExperiment, TSCAN, velociraptor, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, SingleRBook dependencyCount: 57 Package: scRecover Version: 1.8.0 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 MD5sum: 6dbad72419123ad24c2ed6379233aa95 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_13 git_last_commit: ac1750d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scRecover_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scRecover_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scRecover_1.8.0.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: 78 Package: scRepertoire Version: 1.2.0 Depends: ggplot2, R (>= 4.0) Imports: Biostrings, dplyr, reshape2, ggalluvial, stringr, vegan, powerTCR, SummarizedExperiment, plyr, parallel, doParallel, methods, utils, rlang Suggests: knitr, rmarkdown, BiocStyle, scater, circlize, scales, Seurat License: Apache License 2.0 MD5sum: c2880c8369e012b0bd134117efc77a3b 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_13 git_last_commit: 1daa495 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scRepertoire_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scRepertoire_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scRepertoire_1.2.0.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: 85 Package: scruff Version: 1.10.0 Depends: R (>= 3.5.0) 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: b7b5876be10e87f597631689e20bda8a 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_13 git_last_commit: 027572f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scruff_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scruff_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scruff_1.10.0.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: 159 Package: scry Version: 1.4.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: c7661bbefcc9573eef5286b01f462da9 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_13 git_last_commit: e03f3cb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scry_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scry_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scry_1.4.0.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: 46 Package: scTensor Version: 2.2.0 Depends: R (>= 3.5.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor, rTensor, abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi, grDevices, graphics, stats, utils, outliers, Category, meshr, GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 Archs: i386, x64 MD5sum: 65c04027bab742332c076821697b6039 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_13 git_last_commit: adb0ecb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scTensor_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTensor_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTensor_2.2.0.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: 312 Package: scTGIF Version: 1.6.0 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 Archs: i386, x64 MD5sum: 695dcf8f94dd67eab96dc0dcdce679b6 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_13 git_last_commit: 8463e50 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scTGIF_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTGIF_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTGIF_1.6.0.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: 207 Package: scTHI Version: 1.4.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown License: GPL-2 MD5sum: e70e426c5e6a1602bef23ee7a214e484 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_13 git_last_commit: 7d5a24a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/scTHI_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTHI_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTHI_1.4.0.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: 15 Package: scuttle Version: 1.2.1 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: b0271029c4db5f943c0832a78b9536bd 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_13 git_last_commit: 56a8a81 git_last_commit_date: 2021-08-04 Date/Publication: 2021-08-05 source.ver: src/contrib/scuttle_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scuttle_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scuttle_1.2.1.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, mia, mumosa, muscat, scDblFinder, velociraptor suggestsMe: bluster, SingleR, snifter, splatter, TSCAN, HCAData, MouseThymusAgeing, SingleRBook linksToMe: DropletUtils, scran dependencyCount: 38 Package: SDAMS Version: 1.12.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: efda431db8c119d83464084835525b8d 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_13 git_last_commit: 0dc63b2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SDAMS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SDAMS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SDAMS_1.12.0.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: 63 Package: sechm Version: 1.0.0 Depends: R (>= 4.1) Imports: S4Vectors, SummarizedExperiment, seriation, ComplexHeatmap, circlize, methods, randomcoloR, stats, grid, grDevices Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: bf04e5d9c73bc928a529cde0bd769b4c 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_13 git_last_commit: a0a1394 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sechm_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sechm_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sechm_1.0.0.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 dependencyCount: 68 Package: segmentSeq Version: 2.26.0 Depends: R (>= 3.0.0), 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: f51561954ad849fee2835cebd23e81e5 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_13 git_last_commit: 5fe3637 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/segmentSeq_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/segmentSeq_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/segmentSeq_2.26.0.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: 50 Package: selectKSigs Version: 1.4.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: fba91fbf9649aa4accf23487d9c8facb 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_13 git_last_commit: 803d736 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/selectKSigs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/selectKSigs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/selectKSigs_1.4.0.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: 125 Package: SELEX Version: 1.24.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: 706272d74f4c881307821a0b3337f5d0 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_13 git_last_commit: d37bba9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SELEX_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SELEX_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SELEX_1.24.0.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: 20 Package: SemDist Version: 1.26.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) Archs: i386, x64 MD5sum: d5798bdf57dd47e0a65f77d6416d4f6b 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_13 git_last_commit: d508e59 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SemDist_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SemDist_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SemDist_1.26.0.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: 50 Package: semisup Version: 1.16.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 96636288ae16aeae3bac66d95f963fdb 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_13 git_last_commit: 6853d6d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/semisup_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/semisup_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/semisup_1.16.0.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: 1.12.0 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: 184b7c8d42e71e351a4c7b1d94009aa3 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_13 git_last_commit: 6fe3b44 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SEPIRA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SEPIRA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SEPIRA_1.12.0.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: 1.24.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: f009a447b9f202320947cf5015def361 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_13 git_last_commit: 61596ef git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seq2pathway_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seq2pathway_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seq2pathway_1.24.0.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: 127 Package: SeqArray Version: 1.32.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.23.5) 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 Archs: i386, x64 MD5sum: 442b2eea24758d0c388293e7c1f4aef6 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_13 git_last_commit: 8fa3c99 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SeqArray_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqArray_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqArray_1.32.0.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: GDSArray, GENESIS, VariantExperiment, GMMAT, MAGEE suggestsMe: DelayedDataFrame, HIBAG, VCFArray dependencyCount: 21 Package: seqbias Version: 1.40.0 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: ef4494289d1301883e1c57eec705cc86 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_13 git_last_commit: c9d8e78 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqbias_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/seqbias_1.40.0.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: 21 Package: seqCAT Version: 1.14.1 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 MD5sum: ed8a64f4a0f611321e3dd1ccef035ea7 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_13 git_last_commit: a806d9f git_last_commit_date: 2021-10-10 Date/Publication: 2021-10-12 source.ver: src/contrib/seqCAT_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqCAT_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/seqCAT_1.14.1.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: 114 Package: seqCNA Version: 1.38.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 MD5sum: dca2bdb34175f6a6714568930f55c109 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_13 git_last_commit: 9827ffc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqCNA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqCNA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqCNA_1.38.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R suggestsMe: Herper dependencyCount: 26 Package: seqcombo Version: 1.14.1 Depends: R (>= 3.4.0) Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr, methods, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 41787b7e2ad5a2fdbf3d5907b03c8e11 NeedsCompilation: no Title: Visualization Tool for Sequence Recombination and Reassortment Description: Provides useful functions for visualizing sequence recombination and 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_13 git_last_commit: 11fdb25 git_last_commit_date: 2021-08-20 Date/Publication: 2021-08-22 source.ver: src/contrib/seqcombo_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqcombo_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/seqcombo_1.14.1.tgz vignettes: vignettes/seqcombo/inst/doc/reassortment.html, vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment, seqcombo introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/reassortment.R, vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 58 Package: SeqGate Version: 1.2.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: fb84b76f0ebe5901e63169ea98d88299 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_13 git_last_commit: 7196a0b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SeqGate_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqGate_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqGate_1.2.0.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: 27 Package: SeqGSEA Version: 1.32.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 671ace3299db344c315c79e224fbe39f 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_13 git_last_commit: 2ecabac git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SeqGSEA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqGSEA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqGSEA_1.32.0.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: 1.58.0 Depends: methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) Archs: i386, x64 MD5sum: 5173c200a36b6225023304bf88e64d08 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_13 git_last_commit: acb7150 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqLogo_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqLogo_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqLogo_1.58.0.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: 1.24.0 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: eacbee961c406327b499b87af45ddc67 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_13 git_last_commit: 21a9de6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqPattern_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqPattern_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqPattern_1.24.0.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: 22 Package: seqsetvis Version: 1.12.0 Depends: R (>= 3.6), ggplot2 Imports: 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, cowplot, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 21f0219336bb7f1aa4895d0e600e2998 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_13 git_last_commit: f9979c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqsetvis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqsetvis_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqsetvis_1.12.0.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: 90 Package: SeqSQC Version: 1.14.0 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: df93718f9a6f180e3d6a331897ebb905 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_13 git_last_commit: 098b7a8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SeqSQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqSQC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqSQC_1.14.0.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: 140 Package: seqTools Version: 1.26.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9e5ca21ca6164308d9057fd6763423d5 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_13 git_last_commit: f74d126 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/seqTools_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqTools_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqTools_1.26.0.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: 1.30.0 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: 167a615f78e4bba965f975c1b9e4fa4e 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_13 git_last_commit: 0617a90 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SeqVarTools_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqVarTools_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqVarTools_1.30.0.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: 59 Package: sesame Version: 1.10.5 Depends: R (>= 4.1), sesameData, methods Imports: BiocParallel, grDevices, utils, stringr, tibble, illuminaio, MASS, GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, randomForest, wheatmap, ggplot2, KernSmooth, graphics, parallel, matrixStats, DNAcopy, stats, SummarizedExperiment Suggests: scales, knitr, rmarkdown, testthat, dplyr, tidyr, BiocStyle, IlluminaHumanMethylation450kmanifest, minfi, FlowSorted.CordBloodNorway.450k, FlowSorted.Blood.450k, HDF5Array License: MIT + file LICENSE MD5sum: b5868347f3d4c1cc447538a0d05b417b 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 more accurate detection calling, intelligenet inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Hui Shen [aut], Timothy Triche [ctb], Bret Barnes [ctb] 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_13 git_last_commit: 867cb46 git_last_commit_date: 2021-10-07 Date/Publication: 2021-10-10 source.ver: src/contrib/sesame_1.10.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/sesame_1.10.5.zip mac.binary.ver: bin/macosx/contrib/4.1/sesame_1.10.5.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/other.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", 3. Modeling, 2. Non-human Array, 5. Other Features, 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/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/other.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: TCGAbiolinksGUI suggestsMe: MethReg, RnBeads, TCGAbiolinks, sesameData dependencyCount: 133 Package: SEtools Version: 1.6.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, data.table, seriation, ComplexHeatmap, circlize, methods, BiocParallel, randomcoloR, edgeR, openxlsx, sva, stats, DESeq2, Matrix, grid Suggests: BiocStyle, knitr, rmarkdown, ggplot2, pheatmap License: GPL MD5sum: 83a46f296d560d66298c6e161e719b08 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 and plotting functions. In particular, SEtools offers a simple interface for plotting complex heatmaps from SE objects. biocViews: GeneExpression, Visualization 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_13 git_last_commit: 486c252 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SEtools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SEtools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SEtools_1.6.0.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: 122 Package: sevenbridges Version: 1.22.0 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: f3dafe195ba0481c624f78c1ec3e126f 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_13 git_last_commit: 5515fa3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sevenbridges_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sevenbridges_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sevenbridges_1.22.0.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: 27 Package: sevenC Version: 1.12.0 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: 56aad61b2fe15a4e2d50663d6e4e0faf 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_13 git_last_commit: 04cd8e5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sevenC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sevenC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sevenC_1.12.0.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: 74 Package: SGSeq Version: 1.26.0 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 Archs: i386, x64 MD5sum: 73bad464689f411b2087fda1dae915a2 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_13 git_last_commit: d7ca914 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SGSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SGSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SGSeq_1.26.0.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: 98 Package: SharedObject Version: 1.6.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 023ed3087c1fce2f829e7e4093ddb629 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] 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_13 git_last_commit: a29d26f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SharedObject_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SharedObject_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SharedObject_1.6.0.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: 8 Package: shinyepico Version: 1.0.0 Depends: R (>= 4.0.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.0), 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 (>= 0.4.0), 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 (>= 1.1.0), 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: 55b3939a53b97ddab73c66d563c540e3 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_13 git_last_commit: 42ea0c9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/shinyepico_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/shinyepico_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/shinyepico_1.0.0.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: 201 Package: shinyMethyl Version: 1.28.0 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 MD5sum: 3881862cf3146db20b449950b0727153 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_13 git_last_commit: 2634641 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/shinyMethyl_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/shinyMethyl_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/shinyMethyl_1.28.0.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: 153 Package: ShortRead Version: 1.50.0 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: 35f3f8799221414b14887fa77c2b12ae 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_13 git_last_commit: 31dea4d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ShortRead_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ShortRead_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ShortRead_1.50.0.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, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, MACPET, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, systemPipeRdata, genBaRcode suggestsMe: BiocParallel, CSAR, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, yeastRNASeq dependencyCount: 43 Package: SIAMCAT Version: 1.12.0 Depends: R (>= 3.6.0), mlr, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 MD5sum: 1308dd5fe458e7128042b17e5eb242dd 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_13 git_last_commit: fb65d54 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SIAMCAT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIAMCAT_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIAMCAT_1.12.0.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: 99 Package: SICtools Version: 1.22.0 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: b10eff32562f9c91bcf01c0298bb1f9d 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_13 git_last_commit: b9dc057 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SICtools_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/SICtools_1.22.0.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: 2.24.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 3bb323ac98bf808e836812d4d86ced19 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_13 git_last_commit: 09d85c0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SigCheck_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigCheck_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigCheck_2.24.0.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: 1.10.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr License: GPL Archs: i386, x64 MD5sum: 4f3bb4146ceb9cda1c4a1009669d6552 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_13 git_last_commit: 61ee3c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sigFeature_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigFeature_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigFeature_1.10.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.pdf vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 62 Package: SigFuge Version: 1.30.0 Depends: R (>= 3.1.1), 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: 7a85ad9f5437edf8513e5487ba514612 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_13 git_last_commit: bccaaff git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SigFuge_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigFuge_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigFuge_1.30.0.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: 56 Package: siggenes Version: 1.66.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: 2b7ffc929994423c2d5a12f44f1c6575 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_13 git_last_commit: 7784d06 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/siggenes_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/siggenes_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/siggenes_1.66.0.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: coexnet, DAPAR, minfi, trio, XDE, DeSousa2013, INCATome suggestsMe: GCSscore, logicFS dependencyCount: 17 Package: sights Version: 1.18.0 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 Archs: i386, x64 MD5sum: e083c06de9dbd3bf9fc9cee06bf5492a 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_13 git_last_commit: f914a9b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sights_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sights_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sights_1.18.0.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: 45 Package: signatureSearch Version: 1.6.3 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: 53ab9b18308a9bcc86e6b838dc4f3d04 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 [cre, aut], Thomas Girke [aut] Maintainer: Yuzhu Duan 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_13 git_last_commit: 5c1eebb git_last_commit_date: 2021-08-29 Date/Publication: 2021-08-31 source.ver: src/contrib/signatureSearch_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/signatureSearch_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.1/signatureSearch_1.6.3.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 importsMe: signatureSearchData dependencyCount: 176 Package: signeR Version: 1.18.1 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: 7284cac3e0cdb1e94e55f6f7f39ced5a 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_13 git_last_commit: 9672acc git_last_commit_date: 2021-10-07 Date/Publication: 2021-10-10 source.ver: src/contrib/signeR_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/signeR_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/signeR_1.18.1.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: 137 Package: sigPathway Version: 1.60.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 MD5sum: 44f6036756791646d6149bc56f8b2bc7 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_13 git_last_commit: da5e41f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sigPathway_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigPathway_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigPathway_1.60.0.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: 1.6.0 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 MD5sum: 0a2c71697f0936e0ad2b5c730b213763 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_13 git_last_commit: 1f4a968 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SigsPack_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigsPack_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigsPack_1.6.0.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: 99 Package: sigsquared Version: 1.24.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 13203fae17cd1b6b11e42374b21f4f35 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_13 git_last_commit: a15362d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sigsquared_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigsquared_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigsquared_1.24.0.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: 13 Package: SIM Version: 1.62.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: 2e9af981114a628b765c5706f4fdd462 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_13 git_last_commit: bb5067c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SIM_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIM_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIM_1.62.0.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: 62 Package: SIMAT Version: 1.24.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: c04b51976afea5e0d799eca4bf95b577 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_13 git_last_commit: 07b989a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SIMAT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMAT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMAT_1.24.0.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: 52 Package: SimBindProfiles Version: 1.30.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 Archs: i386, x64 MD5sum: 3933dd9a99d914399277577aaec98399 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_13 git_last_commit: be78d26 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SimBindProfiles_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SimBindProfiles_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SimBindProfiles_1.30.0.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: 85 Package: SIMD Version: 1.10.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: c099c6048117198b55bd6a23460901db 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_13 git_last_commit: 8cdcfb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SIMD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMD_1.10.0.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: 1.4.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 77b81fd8b2e10b8988888ec32c3a1f1d 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_13 git_last_commit: 13e0b8e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SimFFPE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SimFFPE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SimFFPE_1.4.0.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: 51 Package: similaRpeak Version: 1.24.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 8406f9754e95113adfaf740e874729ca NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes 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_13 git_last_commit: d820374 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/similaRpeak_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/similaRpeak_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/similaRpeak_1.24.0.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: 1.18.0 Depends: R (>= 4.0.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE Archs: i386, x64 MD5sum: 734b3b5c22a8f8e21c2844d468256c1a 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_13 git_last_commit: e3f8bf7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SIMLR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMLR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMLR_1.18.0.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: 1.2.0 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) 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 License: MIT + file LICENSE MD5sum: ebadd3d3382e872cd815e610dd819f2b 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 provideds functionalities 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_13 git_last_commit: 02e5cb4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/simplifyEnrichment_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/simplifyEnrichment_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/simplifyEnrichment_1.2.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html vignetteTitles: 3. A Shiny app to interactively visualize clustering results, 1. Simplify Functional Enrichment Results, 2. 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 dependencyCount: 77 Package: sincell Version: 1.24.0 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) MD5sum: cf6a32fe3b98a9a1ba156ce6f6617a9c 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_13 git_last_commit: 726ccb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sincell_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sincell_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sincell_1.24.0.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: 63 Package: SingleCellExperiment Version: 1.14.1 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq, Rtsne License: GPL-3 Archs: i386, x64 MD5sum: a05d5bafecd62bc1657cef4762ffa200 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_13 git_last_commit: 5357eff git_last_commit_date: 2021-05-21 Date/Publication: 2021-05-21 source.ver: src/contrib/SingleCellExperiment_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleCellExperiment_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleCellExperiment_1.14.1.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, CellBench, CelliD, CellTrails, CHETAH, clusterExperiment, cydar, cytomapper, DropletUtils, ExperimentSubset, iSEE, LoomExperiment, MAST, mia, mumosa, POWSC, scAlign, scater, scClassifR, scGPS, schex, scPipe, scran, scuttle, singleCellTK, SpatialExperiment, splatter, switchde, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseGastrulationData, MouseThymusAgeing, muscData, scRNAseq, TENxBrainData, TENxPBMCData, TMExplorer, OSCA.intro, DIscBIO, imcExperiment importsMe: ADImpute, aggregateBioVar, airpart, bayNorm, BEARscc, ccfindR, celda, CellMixS, ChromSCape, CiteFuse, clustifyr, CoGAPS, conclus, condiments, corral, distinct, dittoSeq, escape, fcoex, FEAST, GSVA, HIPPO, ILoReg, infercnv, IRISFGM, iSEEu, LineagePulse, mbkmeans, MetaNeighbor, miloR, miQC, muscat, Nebulosa, netSmooth, NewWave, peco, phemd, pipeComp, SC3, SCArray, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, scone, scp, scruff, scry, scTensor, scTGIF, slalom, slingshot, Spaniel, SPsimSeq, tradeSeq, treekoR, velociraptor, waddR, zellkonverter, scpdata, SingleCellMultiModal, spatialLIBD, digitalDLSorteR, SC.MEB suggestsMe: CellaRepertorium, DEsingle, EWCE, FCBF, fishpond, HDF5Array, InteractiveComplexHeatmap, M3Drop, mistyR, MOFA2, ontoProc, phenopath, progeny, PubScore, QFeatures, scFeatureFilter, scPCA, scRecover, SingleR, dorothea, DuoClustering2018, TabulaMurisData, simpleSingleCell, clustree, dyngen, Seurat, singleCellHaystack dependencyCount: 26 Package: SingleCellSignalR Version: 1.4.0 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: d6b1cbf7ec7cf62be10f10e26a78dd99 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_13 git_last_commit: 7bf26c8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SingleCellSignalR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleCellSignalR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleCellSignalR_1.4.0.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: 95 Package: singleCellTK Version: 2.2.0 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, RColorBrewer, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, sva, reshape2, AnnotationDbi, shinyalert, circlize, enrichR, celda, shinycssloaders, uwot, 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 Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, xtable, spelling, org.Mm.eg.db, stringr, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics License: MIT + file LICENSE MD5sum: 3e67e0d2a11756025f15cc2b140b6f2c NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: Run common single cell analysis in the R console or directly through your browser. Includes many functions for import, quality control, normalization, batch correction, clustering, differential expression, and visualization.. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology Author: David Jenkins [aut] (), Vidya Akavoor [aut], Salam Alabdullatif [aut], Shruthi Bandyadka [aut], Emma Briars [aut] (), Xinyun Cao [aut], Sebastian Carrasco Pro [aut], Tyler Faits [aut], Rui Hong [aut], Mohammed Muzamil Khan [aut], Yusuke Koga [aut, cre], Anastasia Leshchyk [aut], Irzam Sarfraz [aut], Yichen Wang [aut], Zhe Wang [aut], W. Evan Johnson [aut] (), Joshua David Campbell [aut] Maintainer: Yusuke Koga URL: https://compbiomed.github.io/sctk_docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_13 git_last_commit: 9fdc1a1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/singleCellTK_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/singleCellTK_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/singleCellTK_2.2.0.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: 351 Package: SingleMoleculeFootprinting Version: 1.0.0 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 Archs: i386, x64 MD5sum: 36073f2fa22ff5a8137ef7c7e7de5e82 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_13 git_last_commit: 398e2fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SingleMoleculeFootprinting_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleMoleculeFootprinting_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleMoleculeFootprinting_1.0.0.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: 109 Package: SingleR Version: 1.6.1 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: ad8acc6b74914c72fd362292b7531708 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_13 git_last_commit: edbe717 git_last_commit_date: 2021-05-20 Date/Publication: 2021-05-20 source.ver: src/contrib/SingleR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleR_1.6.1.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: 43 Package: singscore Version: 1.12.0 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: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 44aa4b9d0f5a7b9dc97e0998816e1225 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_13 git_last_commit: 60052d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/singscore_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/singscore_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/singscore_1.12.0.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: 114 Package: SISPA Version: 1.22.0 Depends: R (>= 3.5),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: ee1eabc775184418f1a14960602c3fbb 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_13 git_last_commit: 9b3f6c1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SISPA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SISPA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SISPA_1.22.0.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: 1.0.1 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, RMySQL, RUnit License: Artistic-2.0 MD5sum: 0bf90d38f37d397a68a067543fb5eea5 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_13 git_last_commit: 73efbf8 git_last_commit_date: 2021-10-06 Date/Publication: 2021-10-07 source.ver: src/contrib/sitadela_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/sitadela_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/sitadela_1.0.2.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: 96 Package: sitePath Version: 1.8.4 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: de1f65084b37fc26f12c9780d5ee9caf NeedsCompilation: yes Title: Phylogenetic pathway–dependent recognition of fixed substitutions and parallel mutations Description: The package does hierarchical search for fixation and parallel mutations given multiple sequence alignment and phylogenetic tree. The package also provides visualization of these mutations on the tree. 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_13 git_last_commit: cb9e231 git_last_commit_date: 2021-09-16 Date/Publication: 2021-09-16 source.ver: src/contrib/sitePath_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/sitePath_1.8.4.zip mac.binary.ver: bin/macosx/contrib/4.1/sitePath_1.8.4.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: 66 Package: sizepower Version: 1.62.0 Depends: stats License: LGPL MD5sum: 92f042ebf78560dcf33d88bb9902cd33 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_13 git_last_commit: babef3f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sizepower_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sizepower_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sizepower_1.62.0.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: 1.24.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Archs: i386, x64 MD5sum: 4a7c02b74107d623429d5360f860fc7d 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_13 git_last_commit: 0f47238 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/skewr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/skewr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/skewr_1.24.0.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: 170 Package: slalom Version: 1.14.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rhdf5, scater, testthat License: GPL-2 MD5sum: f295ea653da321e38b62b073241d31c4 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. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome 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_13 git_last_commit: 926621e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/slalom_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/slalom_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/slalom_1.14.0.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: 86 Package: SLGI Version: 1.52.0 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 MD5sum: eaa2edf430afbe3d0059d03cb35de9bf NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/SLGI git_branch: RELEASE_3_13 git_last_commit: cdf0469 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SLGI_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SLGI_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SLGI_1.52.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependencyCount: 61 Package: slingshot Version: 2.0.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: 285520de7c3556790e5ccf8e5172fe7f 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_13 git_last_commit: ffcbc53 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/slingshot_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/slingshot_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/slingshot_2.0.0.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 dependencyCount: 33 Package: slinky Version: 1.10.0 Depends: R (>= 3.5.0) Imports: SummarizedExperiment, curl, dplyr, foreach, httr, stats, utils, methods, readr, rhdf5, jsonlite, tidyr Suggests: GeoDE, doParallel, testthat, knitr, rmarkdown, ggplot2, Rtsne, Biobase, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: e4120476b853a0c9b0768849d2269c60 NeedsCompilation: no Title: Putting the fun in LINCS L1000 data analysis Description: Wrappers to query the L1000 metadata available via the clue.io REST API as well as helpers for dealing with LINCS gctx files, extracting data sets of interest, converting to SummarizedExperiment objects, and some facilities for performing streamlined differential expression analysis of these data sets. biocViews: DataImport, ThirdPartyClient, GeneExpression, DifferentialExpression, GeneSetEnrichment, PatternLogic Author: Eric J. Kort Maintainer: Eric J. Kort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slinky git_branch: RELEASE_3_13 git_last_commit: 53b81e6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/slinky_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/slinky_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/slinky_1.10.0.tgz vignettes: vignettes/slinky/inst/doc/LINCS-analysis.html vignetteTitles: "LINCS analysis with slinky" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/slinky/inst/doc/LINCS-analysis.R dependencyCount: 69 Package: SLqPCR Version: 1.58.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 5672ec5e6653c7498c8bb5609a3f4400 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_13 git_last_commit: b95b1ed git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SLqPCR_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SLqPCR_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SLqPCR_1.58.0.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: 1.8.0 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: f94e7e036785cc89499eb93002b6d9a0 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_13 git_last_commit: 5591339 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SMAD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMAD_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMAD_1.8.0.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: 1.56.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 42f2e52005be69f997d48d904b204f54 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_13 git_last_commit: c2fb485 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SMAP_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMAP_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMAP_1.56.0.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: 1.20.0 Depends: R (>= 3.3), 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 License: GPL (>=2) MD5sum: ebd37540c10d608dfe409448f16541a4 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_13 git_last_commit: 7acefe7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SMITE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMITE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMITE_1.20.0.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: 144 Package: SNAGEE Version: 1.32.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: bb43a927814ef1b2d2b2b6571876a50b 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_13 git_last_commit: 09f2b4b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SNAGEE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNAGEE_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNAGEE_1.32.0.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: 1.62.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL MD5sum: dc4c555093b2f68a6484634bd0bca107 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_13 git_last_commit: 17b1083 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/snapCGH_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snapCGH_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snapCGH_1.62.0.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: 96 Package: snapcount Version: 1.4.0 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: 15f53bc108ad315fea67c1a4d9ed1808 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_13 git_last_commit: 148c7b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/snapcount_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snapcount_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snapcount_1.4.0.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: 42 Package: snifter Version: 1.2.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, assertthat Suggests: knitr, rmarkdown, scRNAseq, BiocStyle, scater, scran, scuttle, ggplot2, testthat (>= 3.0.0) License: GPL-3 Archs: i386, x64 MD5sum: 3dc71264942cc698f0728f08261758c3 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_13 git_last_commit: 4e71e65 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/snifter_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snifter_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snifter_1.2.0.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: 23 Package: snm Version: 1.40.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL Archs: i386, x64 MD5sum: 384731e7fda3384194097aa6838e8157 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_13 git_last_commit: c2f5188 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/snm_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snm_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snm_1.40.0.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: 19 Package: SNPediaR Version: 1.18.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: e612b1e7391ee48ac39c544992b17721 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_13 git_last_commit: eda562e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SNPediaR_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/SNPediaR_1.18.0.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: 1.22.0 Depends: R (>= 3.1), 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: 32d0ca4a035248b699ac1a28b1ef438d 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_13 git_last_commit: 562991f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SNPhood_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNPhood_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNPhood_1.22.0.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: 127 Package: SNPRelate Version: 1.26.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: 85ffb42150027aaa9d509c93e83247e8 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_13 git_last_commit: 8998076 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SNPRelate_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNPRelate_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNPRelate_1.26.0.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, dartR, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.42.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: e48c8a8ac7cca7491974954aff591fd7 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_13 git_last_commit: 93cc0eb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/snpStats_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snpStats_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snpStats_1.42.0.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, snpStatsWriter importsMe: DExMA, GeneGeneInteR, gwascat, ldblock, martini, RVS, scoreInvHap, GenomicTools, GenomicTools.fileHandler, GWASbyCluster, LDheatmap, PhenotypeSimulator, snpEnrichment, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, omicRexposome, omicsPrint, VariantAnnotation, adjclust, coloc, genio, pegas dependencyCount: 13 Package: soGGi Version: 1.24.1 Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: 952dfafc6f1447cd464c4ad1e07614d1 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_13 git_last_commit: 24358f6 git_last_commit_date: 2021-08-27 Date/Publication: 2021-08-29 source.ver: src/contrib/soGGi_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/soGGi_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.1/soGGi_1.24.1.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: 85 Package: sojourner Version: 1.6.0 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 MD5sum: bcf2df30289e6f702f3c548f4436d676 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_13 git_last_commit: 2ea1727 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sojourner_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sojourner_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sojourner_1.6.0.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: 109 Package: SomaticSignatures Version: 2.28.0 Depends: R (>= 3.1.0), 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: 9e06f28572384842d5fc399c7d9c07bd 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_13 git_last_commit: 3c360f9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SomaticSignatures_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SomaticSignatures_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SomaticSignatures_2.28.0.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: 165 Package: SOMNiBUS Version: 1.0.0 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: de5afe5f5c5acc1c93f2be3a1a187ae1 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_13 git_last_commit: c53c179 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SOMNiBUS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SOMNiBUS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SOMNiBUS_1.0.0.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: 1.30.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 Archs: i386, x64 MD5sum: fdf79dade82a14613924df9a120110b9 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_13 git_last_commit: 9f7b13a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SpacePAC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpacePAC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpacePAC_1.30.0.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: 31 Package: Spaniel Version: 1.6.0 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 Archs: i386, x64 MD5sum: d13f7bcc9299051c5042cf731b16a078 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_13 git_last_commit: 6303e34 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Spaniel_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Spaniel_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Spaniel_1.6.0.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: 192 Package: sparseDOSSA Version: 1.16.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: cb124cef82cc38cef5dc3b66f3fd3d6e 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_13 git_last_commit: cf8e465 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sparseDOSSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparseDOSSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sparseDOSSA_1.16.0.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: 27 Package: sparseMatrixStats Version: 1.4.2 Depends: MatrixGenerics (>= 1.4.2) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 34bb7ab45929c6d92deac396f794503b 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 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_13 git_last_commit: 1ef80c7 git_last_commit_date: 2021-08-05 Date/Publication: 2021-08-08 source.ver: src/contrib/sparseMatrixStats_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparseMatrixStats_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/sparseMatrixStats_1.4.2.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: DelayedMatrixStats, GSVA, adjclust suggestsMe: MatrixGenerics, scPCA dependencyCount: 11 Package: sparsenetgls Version: 1.10.0 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: beaf248594ba7db298fd33e3a66b7de4 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_13 git_last_commit: 3196fa4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sparsenetgls_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparsenetgls_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sparsenetgls_1.10.0.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: 22 Package: SparseSignatures Version: 2.2.0 Depends: R (>= 4.0.0), 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 MD5sum: 6660d105e52ff1f2f7c7229d22deef2a 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_13 git_last_commit: fbc80a3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SparseSignatures_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SparseSignatures_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SparseSignatures_2.2.0.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: 96 Package: SpatialCPie Version: 1.8.0 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: eff2bb4069f205de4326ecd62522b4ff 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_13 git_last_commit: 13b0098 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SpatialCPie_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialCPie_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialCPie_1.8.0.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: 108 Package: SpatialDecon Version: 1.2.0 Depends: R (>= 4.0.0) Imports: logNormReg, grDevices, stats, utils, graphics, Suggests: testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 4ed5430c8f02583346dd093455cc91b1 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 spatially-resolved gene expression data", Danaher (2020). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics Author: Patrick Danaher [aut, cre] Maintainer: Patrick Danaher VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_13 git_last_commit: df6b718 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SpatialDecon_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialDecon_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialDecon_1.2.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: SpatialDecon_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R dependencyCount: 5 Package: SpatialExperiment Version: 1.2.1 Depends: methods, SingleCellExperiment Imports: BiocFileCache, DropletUtils, rjson, magick, grDevices, S4Vectors, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix License: GPL-3 MD5sum: 6c8e46f6768c4dd7c337a1acd9db49df NeedsCompilation: no Title: S4 Class for Spatial Experiments handling Description: Defines S4 classes for storing data for spatial experiments. Main examples are reported by using seqFISH and 10x-Visium Spatial Gene Expression data. This includes specialized methods for storing, retrieving spatial coordinates, 10x dedicated parameters and their handling. biocViews: DataRepresentation, DataImport, ImmunoOncology, DataRepresentation, Infrastructure, SingleCell, GeneExpression Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L. Crowell [aut], Lukas M. Weber [aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_13 git_last_commit: 625ce87 git_last_commit_date: 2021-06-08 Date/Publication: 2021-06-10 source.ver: src/contrib/SpatialExperiment_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialExperiment_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialExperiment_1.2.1.tgz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Building SpatialExperiment object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: ExperimentSubset, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData importsMe: SingleCellMultiModal suggestsMe: mistyR dependencyCount: 94 Package: spatialHeatmap Version: 1.2.0 Imports: av, BiocFileCache, data.table, DESeq2, edgeR, WGCNA, flashClust, htmlwidgets, genefilter, ggplot2, ggdendro, grImport, grid, gridExtra, gplots, igraph, HDF5Array, rsvg, shiny, dynamicTreeCut, grDevices, graphics, ggplotify, parallel, plotly, rols, rappdirs, stats, SummarizedExperiment, shinydashboard, S4Vectors, utils, visNetwork, methods, xml2, yaml Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, ExpressionAtlas, DT, reshape2, Biobase, GEOquery, shinyWidgets, shinyjs, htmltools, shinyBS, sortable License: Artistic-2.0 Archs: i386, x64 MD5sum: c1eed099b95ad73b966cc1beea36a644 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: 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_13 git_last_commit: a2af50d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/spatialHeatmap_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spatialHeatmap_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spatialHeatmap_1.2.0.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: 179 Package: specL Version: 1.26.0 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: da4b8d13e5cb0a51d1d467a619153189 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_13 git_last_commit: bb4fa57 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/specL_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/specL_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/specL_1.26.0.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: 29 Package: SpeCond Version: 1.46.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: c7fc36621515183dfa9b28ffe9e04e02 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_13 git_last_commit: 1c73337 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SpeCond_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpeCond_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpeCond_1.46.0.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: 49 Package: Spectra Version: 1.2.2 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.23.8) Imports: methods, IRanges, MsCoreUtils (>= 1.3.3), 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, magrittr License: Artistic-2.0 MD5sum: 43203f4c3e8ad28f7c8a4bff0fd7e8c5 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] () 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_13 git_last_commit: ef88705 git_last_commit_date: 2021-10-05 Date/Publication: 2021-10-07 source.ver: src/contrib/Spectra_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/Spectra_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/Spectra_1.2.2.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 suggestsMe: xcms dependencyCount: 25 Package: SpectralTAD Version: 1.8.0 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: fd86478e82813f0254670bd007ca3260 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_13 git_last_commit: 3c8bc95 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SpectralTAD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpectralTAD_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpectralTAD_1.8.0.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: 100 Package: SPEM Version: 1.32.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 5f34ab548c7bf45d9309510932d96834 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_13 git_last_commit: 0f2d973 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SPEM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPEM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPEM_1.32.0.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: 2.44.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 7972ed81e1634771ae479d31633e6966 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_13 git_last_commit: 961f469 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SPIA_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPIA_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPIA_2.44.0.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: 15 Package: spicyR Version: 1.4.0 Depends: R (>= 4.0.0) Imports: ggplot2, concaveman, BiocParallel, spatstat.core, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 6ac985f422bd22e57e036aa2489da432 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_13 git_last_commit: d3143db git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/spicyR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spicyR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spicyR_1.4.0.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: 92 Package: SpidermiR Version: 1.22.1 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks, gdata, MAGeCKFlute,networkD3 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 3125c67f94bf00627f456a07d1074201 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_13 git_last_commit: 45680ec git_last_commit_date: 2021-06-16 Date/Publication: 2021-06-17 source.ver: src/contrib/SpidermiR_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpidermiR_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SpidermiR_1.22.1.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiR.html vignetteTitles: Working with SpidermiR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R importsMe: StarBioTrek dependencyCount: 178 Package: spikeLI Version: 2.52.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 2d2fb94ec4011d76205169c53a4e67cc 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_13 git_last_commit: 5adc5e8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/spikeLI_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spikeLI_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spikeLI_2.52.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spkTools Version: 1.48.0 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: 7936091fb2538cab6fca3d6065dee8a2 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_13 git_last_commit: 71225ca git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/spkTools_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spkTools_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spkTools_1.48.0.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: 10 Package: splatter Version: 1.16.1 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: b99e91b551cc4dbb6a71d5850768e236 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_13 git_last_commit: 62da653 git_last_commit_date: 2021-05-20 Date/Publication: 2021-05-20 source.ver: src/contrib/splatter_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/splatter_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/splatter_1.16.1.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: digitalDLSorteR suggestsMe: NewWave, scone, scPCA, SummarizedBenchmark, bcTSNE dependencyCount: 89 Package: SplicingFactory Version: 1.0.3 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: 79ab8a80c0a6d9946747a9fa7a143515 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/SU-CompBio/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/SU-CompBio/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: RELEASE_3_13 git_last_commit: 3e989bd git_last_commit_date: 2021-06-22 Date/Publication: 2021-06-24 source.ver: src/contrib/SplicingFactory_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/SplicingFactory_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/SplicingFactory_1.0.3.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: 26 Package: SplicingGraphs Version: 1.32.0 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: 1d8c19588396d7b359a8657afbeee3a1 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_13 git_last_commit: 6066f74 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SplicingGraphs_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SplicingGraphs_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SplicingGraphs_1.32.0.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: 99 Package: splineTimeR Version: 1.20.0 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: 7bb1b74dfe5fe9d4da4c6e7b937cc769 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_13 git_last_commit: 8c36a77 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/splineTimeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/splineTimeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/splineTimeR_1.20.0.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: 64 Package: SPLINTER Version: 1.18.0 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: fcdb2d46a368e6252f06584d1641c946 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_13 git_last_commit: 9bb1aee git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SPLINTER_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPLINTER_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPLINTER_1.18.0.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: 146 Package: splots Version: 1.58.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI License: LGPL MD5sum: bdc3a3a788ff017916c1e8feacd6ef63 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_13 git_last_commit: ed2585e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/splots_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/splots_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/splots_1.58.0.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: 1.14.0 Depends: R (>= 3.4) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: 683ca85c876b673c4d303e8042a49b1b 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. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression Author: Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_13 git_last_commit: 30cc6aa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SPONGE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPONGE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPONGE_1.14.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R dependencyCount: 38 Package: spqn Version: 1.4.0 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: a50f08c56ab3f724d233d19e4d4fdc9a 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_13 git_last_commit: 9360415 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/spqn_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spqn_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spqn_1.4.0.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: 59 Package: SPsimSeq Version: 1.2.0 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: e35a1b8bccde92858f94dd0337dd222c 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_13 git_last_commit: 069529a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SPsimSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPsimSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPsimSeq_1.2.0.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 dependencyCount: 134 Package: SQLDataFrame Version: 1.6.0 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: faf88a031df778aa27c6c74df1cdac06 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_13 git_last_commit: f033f7b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SQLDataFrame_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SQLDataFrame_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SQLDataFrame_1.6.0.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: 42 Package: SQUADD Version: 1.42.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 43c86c005b004ccda84f99812055c449 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_13 git_last_commit: 4750b2f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SQUADD_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SQUADD_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SQUADD_1.42.0.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: 1.8.0 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: 83ae8b8a50780210f0ab86e5142eb7e3 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_13 git_last_commit: ba2d598 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sRACIPE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sRACIPE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sRACIPE_1.8.0.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: 84 Package: SRAdb Version: 1.54.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 3d1bf04fbed86cdaab9bdcaeea8b784a 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_13 git_last_commit: d4df032 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SRAdb_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SRAdb_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SRAdb_1.54.0.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: 61 Package: srnadiff Version: 1.12.2 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 MD5sum: 16584d03cad15dd0b0cd83e000bee66f 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_13 git_last_commit: 21bcbca git_last_commit_date: 2021-06-02 Date/Publication: 2021-06-03 source.ver: src/contrib/srnadiff_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/srnadiff_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.1/srnadiff_1.12.2.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: 192 Package: sscore Version: 1.64.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: e407a4a3d1781c400532b5824988cc50 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_13 git_last_commit: cb449f5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sscore_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sscore_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sscore_1.64.0.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: 13 Package: sscu Version: 2.22.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 3acaf959952f008ef66d0c4d5b89f9dd 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_13 git_last_commit: 932927e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sscu_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sscu_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sscu_2.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 27 Package: sSeq Version: 1.30.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 5fca21dbb85c386db77380933ec87b75 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_13 git_last_commit: 4770dd4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sSeq_1.30.0.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 dependencyCount: 3 Package: ssize Version: 1.66.0 Depends: gdata, xtable License: LGPL Archs: i386, x64 MD5sum: 78d4331830d68690c64c6347279d649d 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_13 git_last_commit: 5d7c39b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ssize_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssize_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssize_1.66.0.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 importsMe: maGUI dependencyCount: 6 Package: ssPATHS Version: 1.6.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 2b73ccc7b3d598ecd4bde53e2b89b0a2 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_13 git_last_commit: 32ea581 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ssPATHS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssPATHS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssPATHS_1.6.0.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: 110 Package: ssrch Version: 1.8.1 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 5f16a58680dcf8805999cce85f006167 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_13 git_last_commit: 104d1ae git_last_commit_date: 2021-07-28 Date/Publication: 2021-07-29 source.ver: src/contrib/ssrch_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssrch_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ssrch_1.8.1.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: 40 Package: ssviz Version: 1.26.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats Suggests: knitr License: GPL-2 Archs: i386, x64 MD5sum: eb14c049b6909ee23867606da8f4061c 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_13 git_last_commit: 4bc179f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ssviz_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssviz_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssviz_1.26.0.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: 1.14.0 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: fafad71ca1b27f60f3b6e049a34c7c4b 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_13 git_last_commit: f1df150 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/stageR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/stageR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/stageR_1.14.0.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: 26 Package: STAN Version: 2.20.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) MD5sum: bb98dd9b49f43f6a475af9e928d8f47f 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_13 git_last_commit: 9c7a3b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/STAN_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STAN_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STAN_2.20.0.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: 145 Package: staRank Version: 1.34.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: d57c9c8d51937fc5fc0ab7122a0e538a 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_13 git_last_commit: 7703706 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/staRank_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/staRank_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/staRank_1.34.0.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: 92 Package: StarBioTrek Version: 1.18.0 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: b7ff7a58c036d4d8e3be42416ccc7486 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_13 git_last_commit: 4e39230 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/StarBioTrek_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/StarBioTrek_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/StarBioTrek_1.18.0.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: 188 Package: STATegRa Version: 1.28.0 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: fa125ee1437d5c6ccdf721432fa0d201 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_13 git_last_commit: 0992626 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/STATegRa_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STATegRa_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STATegRa_1.28.0.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: 60 Package: statTarget Version: 1.22.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown, gWidgets2,gWidgets2RGtk2,RGtk2 License: LGPL (>= 3) MD5sum: 704a7e01fb26bc12c98547fde784ba42 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, GUI, 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_13 git_last_commit: b41ec46 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/statTarget_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/statTarget_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/statTarget_1.22.0.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: stepNorm Version: 1.64.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 7936ca44e03ba4fd9f3a92d45ba993c2 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_13 git_last_commit: 5cd3b38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/stepNorm_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/stepNorm_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/stepNorm_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: strandCheckR Version: 1.10.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 7b8536c4046860f71511e9ff3b2ec930 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_13 git_last_commit: c7a42a9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/strandCheckR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/strandCheckR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/strandCheckR_1.10.0.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: 114 Package: Streamer Version: 1.38.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 4d31ee9b82b9a24017ea422d84d327ed 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_13 git_last_commit: c079c02 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Streamer_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Streamer_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Streamer_1.38.0.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: 2.4.2 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 1646a16a5265db675c1ed168672df068 NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://www.string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_13 git_last_commit: 38baec2 git_last_commit_date: 2021-09-17 Date/Publication: 2021-09-19 source.ver: src/contrib/STRINGdb_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/STRINGdb_2.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/STRINGdb_2.4.2.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: coexnet, IMMAN, pwOmics, RITAN, XINA suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 40 Package: STROMA4 Version: 1.16.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: d73a9a5c07b61dc5c55823db1f1d0365 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_13 git_last_commit: 2107780 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/STROMA4_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STROMA4_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STROMA4_1.16.0.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: 17 Package: struct Version: 1.4.0 Depends: R (>= 4.0) Imports: methods, ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: 308eea3f9181f76400bc4af256d0a496 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. STATistics Ontology (STATO) 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_13 git_last_commit: f884f6a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/struct_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/struct_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/struct_1.4.0.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: 37 Package: Structstrings Version: 1.8.0 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: fe9b9b5985b37749489a1c16f312ee4f 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_13 git_last_commit: 77bcc49 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Structstrings_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Structstrings_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Structstrings_1.8.0.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: 23 Package: structToolbox Version: 1.4.3 Depends: R (>= 4.0), struct (>= 1.2.0) 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 License: GPL-3 MD5sum: 2c4ddd9be6d40de23e4d6df96217cf77 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: RELEASE_3_13 git_last_commit: 1af2d87 git_last_commit_date: 2021-09-17 Date/Publication: 2021-09-21 source.ver: src/contrib/structToolbox_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/structToolbox_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/structToolbox_1.4.3.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: 70 Package: StructuralVariantAnnotation Version: 1.8.2 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 MD5sum: 4446a01a271f1138a6a34f47c2e15bcb 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_13 git_last_commit: 325f68f git_last_commit_date: 2021-08-05 Date/Publication: 2021-08-05 source.ver: src/contrib/StructuralVariantAnnotation_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/StructuralVariantAnnotation_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/StructuralVariantAnnotation_1.8.2.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 dependencyCount: 98 Package: SubCellBarCode Version: 1.8.0 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 Archs: i386, x64 MD5sum: 89bb68fd0c8c68e46bea13c2465c111e 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_13 git_last_commit: b5578c6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SubCellBarCode_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SubCellBarCode_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SubCellBarCode_1.8.0.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: 122 Package: subSeq Version: 1.22.0 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: 1baa055fc4b5d7f5981ca392ac4b2f0a 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_13 git_last_commit: 1c1de44 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/subSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/subSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/subSeq_1.22.0.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: 55 Package: SummarizedBenchmark Version: 2.10.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) MD5sum: 9d333a7cd6a7e6e376a309cba58de762 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_13 git_last_commit: 4d7e78f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SummarizedBenchmark_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SummarizedBenchmark_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SummarizedBenchmark_2.10.0.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: 77 Package: SummarizedExperiment Version: 1.22.0 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 (>= 0.27.12), 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, BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 65663a5b4371aa6a1b4bcfb1cdce60ed 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_13 git_last_commit: 7d1110e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SummarizedExperiment_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SummarizedExperiment_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SummarizedExperiment_1.22.0.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, bambu, BDMMAcorrect, BiocSklearn, BiSeq, bnbc, BrainSABER, bsseq, CAGEfightR, celaref, clusterExperiment, compartmap, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, DMRcate, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, ExperimentSubset, ExpressionAtlas, FEAST, FRASER, GenoGAM, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, lefser, lipidr, LoomExperiment, made4, MatrixQCvis, MBASED, methrix, methylPipe, MetNet, mia, miaViz, minfi, miRmine, moanin, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, NewWave, OUTRIDER, padma, PDATK, PhIPData, profileplyr, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scClassifR, scGPS, scone, SDAMS, SeqGate, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, soGGi, spqn, ssPATHS, stageR, SummarizedBenchmark, survtype, tidySummarizedExperiment, TimeSeriesExperiment, TissueEnrich, TNBC.CMS, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, DREAM4, fission, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, parathyroidSE, restfulSEData, sampleClassifierData, spqnData, timecoursedata, DRomics importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, alpine, AlpsNMR, animalcules, anota2seq, APAlyzer, apeglm, appreci8R, ASICS, AUCell, autonomics, awst, barcodetrackR, BASiCS, batchelor, BayesSpace, bayNorm, BBCAnalyzer, bigPint, BiocOncoTK, BioNERO, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BRGenomics, BUMHMM, BUScorrect, CAGEr, CATALYST, cBioPortalData, ccfindR, celda, CelliD, CellMixS, CellTrails, censcyt, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR, CNVRanger, coexnet, CoGAPS, combi, conclus, condiments, consensusDE, CopyNumberPlots, corral, countsimQC, crossmeta, cydar, cytomapper, DAMEfinder, dasper, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, DEWSeq, diffcyt, diffUTR, DiscoRhythm, distinct, dittoSeq, DominoEffect, doppelgangR, doseR, DropletUtils, Dune, easyRNASeq, eisaR, ELMER, ensemblVEP, epialleleR, epigraHMM, epivizrData, erma, EWCE, FCBF, fcScan, fishpond, GARS, gCrisprTools, GeneTonic, GenomicDataCommons, getDEE2, ggbio, Glimma, glmGamPoi, glmSparseNet, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva, icetea, ideal, ILoReg, infercnv, INSPEcT, InterMineR, iSEEu, iteremoval, LACE, LineagePulse, lionessR, MADSEQ, marr, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, methyAnalysis, MethylAid, methylscaper, methylumi, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, MLSeq, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, MsFeatures, msgbsR, MSPrep, msqrob2, MultiDataSet, multiOmicsViz, mumosa, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, netSmooth, NormalyzerDE, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, oncomix, ORFik, OVESEG, PAIRADISE, pcaExplorer, peco, PharmacoGx, phemd, phenopath, PhosR, pipeComp, pmp, POWSC, proActiv, proDA, psichomics, pulsedSilac, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, regionReport, regsplice, rgsepd, Rmmquant, RNAAgeCalc, RNAsense, roar, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview, SC3, SCArray, scater, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scp, scPipe, scran, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, sechm, seqCAT, sesame, SEtools, sigFeature, SigsPack, singscore, slalom, slingshot, slinky, snapcount, SNPhood, Spaniel, SpatialCPie, SpatialExperiment, spatialHeatmap, splatter, SplicingFactory, srnadiff, struct, StructuralVariantAnnotation, supersigs, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, tidybulk, tidySingleCellExperiment, TOAST, tomoda, ToxicoGx, tradeSeq, TrajectoryUtils, TraRe, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, tscR, TSRchitect, TTMap, TVTB, tximeta, VariantFiltering, vidger, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, emtdata, FieldEffectCrc, GSE13015, HMP2Data, IHWpaper, MetaGxBreast, scRNAseq, SingleCellMultiModal, spatialLIBD, TCGAWorkflowData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, BinQuasi, digitalDLSorteR, HeritSeq, imcExperiment, microbial, PlasmaMutationDetector, pulseTD, SC.MEB suggestsMe: AnnotationHub, biobroom, BiocPkgTools, dcanr, dce, dearseq, DelayedArray, edgeR, EnMCB, epivizr, epivizrChart, esetVis, fobitools, GENIE3, GenomicRanges, globalSeq, gsean, HDF5Array, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, MatrixGenerics, mistyR, MOFA2, MSnbase, pathwayPCA, podkat, PubScore, RiboProfiling, S4Vectors, scFeatureFilter, semisup, systemPipeShiny, TFutils, biotmleData, curatedAdipoArray, dorothea, DuoClustering2018, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, clustree, conos, dyngen, polyRAD, RaceID, seqgendiff, Seurat, Signac, singleCellHaystack dependencyCount: 25 Package: Summix Version: 1.0.3 Depends: R (>= 4.1) Imports: nloptr, methods Suggests: rmarkdown, markdown, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: a553015627d5bf8ce6aef0aa69e15a84 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_13 git_last_commit: 1eb1cf6 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/Summix_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/Summix_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/Summix_1.0.3.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: 2 Package: supersigs Version: 1.0.0 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: f73998c7f13911cff20d71efaff3a9b1 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_13 git_last_commit: 8ddfdfe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/supersigs_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/supersigs_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/supersigs_1.0.0.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: 102 Package: supraHex Version: 1.30.0 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 96e2c826e6fdde0826d446df22ac9ca2 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_13 git_last_commit: 67ac896 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/supraHex_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/supraHex_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/supraHex_1.30.0.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: TCGAbiolinks dependencyCount: 48 Package: survcomp Version: 1.42.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, CPE, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: 9ac61aa20b66250ddb0508d92f1fd685 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, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush, Samuel Branders, Zhaleh Safikhani Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_13 git_last_commit: 2b6bd69 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/survcomp_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/survcomp_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/survcomp_1.42.0.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: 1.8.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 705baac0652cb4144a2dc4e49504f4d2 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_13 git_last_commit: 3deeff9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/survtype_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/survtype_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/survtype_1.8.0.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: 149 Package: Sushi Version: 1.30.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: 9840a3797be862b490e7070e54bc68ec 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 git_url: https://git.bioconductor.org/packages/Sushi git_branch: RELEASE_3_13 git_last_commit: 817131b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Sushi_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Sushi_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Sushi_1.30.0.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: ChromSCape, diffloop, Ularcirc, VaSP dependencyCount: 75 Package: sva Version: 3.40.0 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: 61356712b8764b92a8d0a63067c10b01 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_13 git_last_commit: 3165ab9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/sva_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sva_3.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sva_3.40.0.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, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, cinaR, DGEobj.utils, dSVA, oncoPredict, seqgendiff suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, FieldEffectCrc, CAGEWorkflow, SuperLearner dependencyCount: 68 Package: SWATH2stats Version: 1.22.0 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: 735cf70e3497bb6fd6b917b5117faa72 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_13 git_last_commit: 9dd20d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SWATH2stats_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SWATH2stats_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SWATH2stats_1.22.0.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: 92 Package: SwathXtend Version: 2.14.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: f031129eba2860feea7dd69c74f332a3 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_13 git_last_commit: 32e1647 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SwathXtend_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SwathXtend_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SwathXtend_2.14.0.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: 1.18.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 5134ba7f6a3ea7b9155b713999394d1f 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_13 git_last_commit: 3011931 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/swfdr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/swfdr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/swfdr_1.18.0.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: SwimR Version: 1.29.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 Archs: i386, x64 MD5sum: 65e1d5ce3394de3c0c8dadc82734f2af NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely git_url: https://git.bioconductor.org/packages/SwimR git_branch: master git_last_commit: fd53d6c git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-19 source.ver: src/contrib/SwimR_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SwimR_1.29.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SwimR_1.29.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R dependencyCount: 14 Package: switchBox Version: 1.28.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: 52bf8e1f62c9a08a7bfaac0034aeeea6 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_13 git_last_commit: bc4ba73 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/switchBox_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/switchBox_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/switchBox_1.28.0.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: 1.18.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: d66ec434655528af5813e453f0262755 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_13 git_last_commit: a66a9eb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/switchde_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/switchde_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/switchde_1.18.0.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: 61 Package: synergyfinder Version: 3.0.14 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 Archs: i386, x64 MD5sum: 8ec86900ffaf2ba8abf49ff8cc1fdec1 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. 1. Synergy scores valuculation via all the popular models, including HSA, Loewe, Bliss and ZIP; 2. Drug Sensitivity Score (CSS) and Relitave Inhibition (RI) for drug sensitivity evaluation; 3. Visualization for drug combination matrices and scores. Based on this package, we also provide a web application (https://synergyfinderplus.org/) for users who prefer more friendly 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_13 git_last_commit: 11d2c59 git_last_commit_date: 2021-10-13 Date/Publication: 2021-10-14 source.ver: src/contrib/synergyfinder_3.0.14.tar.gz win.binary.ver: bin/windows/contrib/4.1/synergyfinder_3.0.14.zip mac.binary.ver: bin/macosx/contrib/4.1/synergyfinder_3.0.14.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: 186 Package: SynExtend Version: 1.4.1 Depends: R (>= 4.0.0), DECIPHER (>= 2.18.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats Suggests: BiocStyle, knitr, markdown, rtracklayer, igraph, rmarkdown License: GPL-3 MD5sum: 6240e1d4a5d995682239bae7210133d4 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] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_13 git_last_commit: 47bd51d git_last_commit_date: 2021-05-27 Date/Publication: 2021-05-30 source.ver: src/contrib/SynExtend_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SynExtend_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SynExtend_1.4.1.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: 1.22.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: 88ddeaa10d28c654a7e18dfa9c52d001 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_13 git_last_commit: c97772b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/synlet_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/synlet_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/synlet_1.22.0.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: 72 Package: SynMut Version: 1.8.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 94aefa02efa87ba3ef00271d526bc995 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_13 git_last_commit: ea7c78c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/SynMut_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SynMut_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SynMut_1.8.0.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: 1.26.3 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, GenomicFeatures (>= 1.31.3), SummarizedExperiment, VariantAnnotation (>= 1.25.11), rjson, ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, batchtools, yaml, stringr, assertthat, magrittr, DOT, rsvg, IRanges, testthat, S4Vectors, crayon Suggests: BiocGenerics, ape, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel, BiocManager, systemPipeRdata, GenomicAlignments, grid, DT, dplyr, kableExtra License: Artistic-2.0 MD5sum: 5b496836f16641afc7d7a24cf69494e3 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/ 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_13 git_last_commit: 08593cd git_last_commit_date: 2021-06-26 Date/Publication: 2021-06-27 source.ver: src/contrib/systemPipeR_1.26.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeR_1.26.3.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeR_1.26.3.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_CWL.html, vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR and CWL, systemPipeR: Workflows collection, systemPipeR: Workflow design and reporting generation environment hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_CWL.R, vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind, RNASeqR suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 159 Package: systemPipeShiny Version: 1.2.0 Depends: R (>= 4.0.0), shiny (>= 1.5.0), spsUtil (>= 0.1.2), spsComps (>= 0.3.0), drawer 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, systemPipeRdata, networkD3, rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DOT, shinyTree, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, ggtree, Rtsne, UpSetR, tidyr, esquisse (>= 1.0.0), cicerone License: GPL (>= 3) Archs: i386, x64 MD5sum: 385f7fe06c286d13f24f28582f2c2031 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_13 git_last_commit: 6765e8d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/systemPipeShiny_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeShiny_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeShiny_1.2.0.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: 122 Package: systemPipeTools Version: 1.0.0 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: b00418e13af36f1a213bb106f3517b3a 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_13 git_last_commit: a3bb4cd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/systemPipeTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeTools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeTools_1.0.0.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: 132 Package: TADCompare Version: 1.2.0 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: 6eea1a0b87f8e11c91f5d710b4b695db 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_13 git_last_commit: 1d1f391 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TADCompare_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TADCompare_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TADCompare_1.2.0.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: 158 Package: TAPseq Version: 1.4.0 Depends: R (>= 4.0) 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 License: MIT + file LICENSE MD5sum: d3c4e3365fc09cd2efff7d0711f8936c 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 Gschwind [aut, cre] (), Lars Velten [aut] (), Lars Steinmetz [aut] Maintainer: Andreas 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_13 git_last_commit: cd5c228 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TAPseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TAPseq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TAPseq_1.4.0.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: 99 Package: target Version: 1.6.0 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: b93ef0fde65cc44770eb0016ab3a7f41 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_13 git_last_commit: e8a5075 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/target_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/target_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/target_1.6.0.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: 48 Package: TargetScore Version: 1.30.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 2c092377935b1f27a2f9153333134d0f 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_13 git_last_commit: c61d77c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TargetScore_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TargetScore_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TargetScore_1.30.0.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: 1.48.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 42d1737a68bb27604fe4cb62c7497437 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_13 git_last_commit: d59a715 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TargetSearch_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TargetSearch_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TargetSearch_1.48.0.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: 1.22.0 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: 6d7b69223c1420f24082260b90c96a7f 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: RELEASE_3_13 git_last_commit: 2f87a51 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TarSeqQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TarSeqQC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TarSeqQC_1.22.0.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: 102 Package: TBSignatureProfiler Version: 1.4.11 Depends: R (>= 4.1) Imports: ASSIGN (>= 1.23.1), GSVA, singscore, methods, ComplexHeatmap, RColorBrewer, ggplot2, S4Vectors, reshape2, ROCit, DESeq2, DT, edgeR, gdata, SummarizedExperiment, magrittr, stats, rlang, BiocParallel, BiocGenerics Suggests: testthat, spelling, lintr, covr, knitr, rmarkdown, BiocStyle, shiny, circlize, caret, dplyr, plyr, impute, sva, glmnet, randomForest, MASS, class, e1071, pROC, HGNChelper License: MIT + file LICENSE MD5sum: 7a19ac0f58e471c088b6ea56900f065b 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. biocViews: GeneExpression, DifferentialExpression Author: David Jenkins [aut], Aubrey Odom [aut, cre], Xutao Wang [aut], Yue Zhao [aut], Christian Love [aut], W. Evan Johnson [aut] Maintainer: Aubrey Odom 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_13 git_last_commit: 2730a24 git_last_commit_date: 2021-10-13 Date/Publication: 2021-10-14 source.ver: src/contrib/TBSignatureProfiler_1.4.11.tar.gz win.binary.ver: bin/windows/contrib/4.1/TBSignatureProfiler_1.4.11.zip mac.binary.ver: bin/macosx/contrib/4.1/TBSignatureProfiler_1.4.11.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 160 Package: TCC Version: 1.32.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 1a33afbaa0c915cf1cb5077af6bea337 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_13 git_last_commit: c9bfbb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TCC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCC_1.32.0.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: 2.20.1 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, readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, pathview, clusterProfiler, 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: 6e9004f115b1b3c48a113d6ef08cfe52 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_13 git_last_commit: d7830c91 git_last_commit_date: 2021-10-04 Date/Publication: 2021-10-07 source.ver: src/contrib/TCGAbiolinks_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinks_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinks_2.20.1.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/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", 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/subtypes.R importsMe: ELMER, MoonlightR, SpidermiR, TCGAbiolinksGUI, SingscoreAMLMutations, TCGAWorkflow suggestsMe: Rediscover dependencyCount: 114 Package: TCGAbiolinksGUI Version: 1.18.0 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, pander License: GPL (>= 3) MD5sum: ba22715a8e237e91b844afc7fea7d8b7 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_13 git_last_commit: 8315ea1 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TCGAbiolinksGUI_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinksGUI_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinksGUI_1.18.0.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: 298 Package: TCGAutils Version: 1.12.0 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: BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, 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: 952ca0341b0cfee2843b3aaa3736b51c 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_13 git_last_commit: d0b9159 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TCGAutils_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAutils_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAutils_1.12.0.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 suggestsMe: CNVRanger, dce, glmSparseNet, netDx, curatedTCGAData dependencyCount: 107 Package: TCseq Version: 1.16.0 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: 6011df00b02a6f375ef21b459ca67458 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_13 git_last_commit: fa7ce98 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TCseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCseq_1.16.0.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: 1.42.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 Archs: i386, x64 MD5sum: 0b64d21387b03e942e3a37f06b22b8c0 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_13 git_last_commit: 2e91540 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TDARACNE_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TDARACNE_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TDARACNE_1.42.0.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: 14 Package: tenXplore Version: 1.14.2 Depends: R (>= 3.4), 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: 13c43f428c6a640a24ef4d164a5fc59f 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_13 git_last_commit: bd72b38 git_last_commit_date: 2021-09-11 Date/Publication: 2021-09-12 source.ver: src/contrib/tenXplore_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/tenXplore_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/tenXplore_1.14.2.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: 119 Package: TEQC Version: 4.14.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 044792a752ca178d0af1d6ad408f820f 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_13 git_last_commit: a05076b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TEQC_4.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TEQC_4.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TEQC_4.14.0.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: 31 Package: ternarynet Version: 1.36.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 5f1514b50dd48633417e9944fcde0c2f 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_13 git_last_commit: cb404d2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ternarynet_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ternarynet_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ternarynet_1.36.0.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: 19 Package: TFARM Version: 1.14.0 Depends: R (>= 3.4) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: a8d292b41bc27b2847135e3d90be2340 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_13 git_last_commit: 8557630 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TFARM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFARM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFARM_1.14.0.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: 64 Package: TFBSTools Version: 1.30.0 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) License: GPL-2 MD5sum: f57defaa5dac8b96286881e0cfd13c35 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_13 git_last_commit: a8d5eba git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TFBSTools_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFBSTools_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFBSTools_1.30.0.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, motifmatchr, primirTSS suggestsMe: MAGAR, MethReg, pageRank, universalmotif, JASPAR2018, JASPAR2020, CAGEWorkflow, Signac dependencyCount: 122 Package: TFEA.ChIP Version: 1.12.0 Depends: R (>= 3.3) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, GSEABase, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: ef8178cfa520b80d703ac561b5c05bb7 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_13 git_last_commit: ef83d9d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TFEA.ChIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFEA.ChIP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFEA.ChIP_1.12.0.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: 100 Package: TFHAZ Version: 1.14.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 045208b9138fb46a5fdab5bd4d8756e1 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, Marco Masseroli Maintainer: Alberto Marchesi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_13 git_last_commit: cee2ddc git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TFHAZ_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFHAZ_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFHAZ_1.14.0.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: 18 Package: TFutils Version: 1.12.2 Depends: R (>= 3.5.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl Suggests: knitr, data.table, testthat, AnnotationDbi, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, org.Hs.eg.db, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 93fcf6da2b85c47c9e175c2d342bc94f NeedsCompilation: no Title: TFutils Description: Package to work with TF metadata from various sources. 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_13 git_last_commit: df013ae git_last_commit_date: 2021-08-03 Date/Publication: 2021-08-05 source.ver: src/contrib/TFutils_1.12.2.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/TFutils_1.12.2.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: 105 Package: tidybulk Version: 1.4.0 Depends: R (>= 4.1.0) Imports: tibble, readr, dplyr, magrittr, tidyr, stringr, rlang, purrr, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, 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 License: GPL-3 MD5sum: 3906f7e8f13c3265494221277511a70c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tidybulk git_branch: RELEASE_3_13 git_last_commit: 945a727 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tidybulk_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidybulk_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tidybulk_1.4.0.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: 1.2.1 Depends: R (>= 4.0.0), SingleCellExperiment Imports: SummarizedExperiment, dplyr, tibble, tidyr, ggplot2, plotly, magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, 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: b571c190a6502d7cc2de95432a850d96 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_13 git_last_commit: 775f980 git_last_commit_date: 2021-08-17 Date/Publication: 2021-08-19 source.ver: src/contrib/tidySingleCellExperiment_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidySingleCellExperiment_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/tidySingleCellExperiment_1.2.1.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 dependencyCount: 84 Package: tidySummarizedExperiment Version: 1.2.0 Depends: R (>= 4.0.0), SummarizedExperiment Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, plotly, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown License: GPL-3 MD5sum: 895eef3b22dd806cf1c4ca437e4725d3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: RELEASE_3_13 git_last_commit: bd649f2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tidySummarizedExperiment_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidySummarizedExperiment_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tidySummarizedExperiment_1.2.0.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: 83 Package: tigre Version: 1.46.0 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 Archs: i386, x64 MD5sum: a654971a7f687ca64bcda45e0185e3ee 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_13 git_last_commit: 95c5a86 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tigre_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tigre_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tigre_1.46.0.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: 53 Package: TileDBArray Version: 1.2.1 Depends: DelayedArray (>= 0.15.16) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 90de61a5f27afcc08ec723ba82ff248d 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_13 git_last_commit: 5c323a8 git_last_commit_date: 2021-05-20 Date/Publication: 2021-05-20 source.ver: src/contrib/TileDBArray_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TileDBArray_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TileDBArray_1.2.1.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: 24 Package: tilingArray Version: 1.70.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 6b4deb38ecc0e78071f9dc8c252e1b00 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_13 git_last_commit: 74c74b4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tilingArray_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tilingArray_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tilingArray_1.70.0.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: 87 Package: timecourse Version: 1.64.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 7e50243c160ee0f480294698f8a12606 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_13 git_last_commit: c91dd66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/timecourse_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timecourse_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timecourse_1.64.0.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: 11 Package: timeOmics Version: 1.4.0 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 MD5sum: adf11988086fee980925ef6f4a7c7337 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: RELEASE_3_13 git_last_commit: 65fbb16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/timeOmics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timeOmics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timeOmics_1.4.0.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 dependencyCount: 72 Package: timescape Version: 1.16.0 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: 5709b478a87c9418dbcf6492cadd4888 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_13 git_last_commit: 9a1760d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/timescape_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timescape_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timescape_1.16.0.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: 33 Package: TimeSeriesExperiment Version: 1.10.1 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: 82e3d869096ef9190c95d3c1f3d2f88e 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: RELEASE_3_13 git_last_commit: 543ffaf git_last_commit_date: 2021-08-31 Date/Publication: 2021-09-02 source.ver: src/contrib/TimeSeriesExperiment_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TimeSeriesExperiment_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TimeSeriesExperiment_1.10.1.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: 130 Package: TimiRGeN Version: 1.2.0 Depends: R (>= 4.0), 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: 7f73b29b0e774d0d5aaf19088098e0c2 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 effecting 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_13 git_last_commit: b07ea29 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-20 source.ver: src/contrib/TimiRGeN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TimiRGeN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TimiRGeN_1.2.0.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: 194 Package: TIN Version: 1.24.0 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 Archs: i386, x64 MD5sum: b26da7a2e19f76170c54670dfcdaaa20 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_13 git_last_commit: 4ee1fa2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TIN_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TIN_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TIN_1.24.0.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: 129 Package: TissueEnrich Version: 1.12.0 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 Archs: i386, x64 MD5sum: fc3b1232ef676afdda4032e536df55cb 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_13 git_last_commit: 27c5ae2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TissueEnrich_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TissueEnrich_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TissueEnrich_1.12.0.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: 89 Package: TitanCNA Version: 1.30.0 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 Archs: i386, x64 MD5sum: f9526f1532050191e426c3b5c39acc0b 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_13 git_last_commit: 4694694 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TitanCNA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TitanCNA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TitanCNA_1.30.0.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: 1.70.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 5ab5977b3f50ec45eb240e0418d7d7e6 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_13 git_last_commit: 0c61420 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tkWidgets_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tkWidgets_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tkWidgets_1.70.0.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: 1.0.0 Depends: R (>= 4.0) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: a009b2743ab766cf5311c42c664da9f5 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_13 git_last_commit: dc3ee89 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tLOH_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tLOH_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tLOH_1.0.0.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: 113 Package: TMixClust Version: 1.14.0 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: 2fb70166b3e562c0c510446af2de6df5 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_13 git_last_commit: 634a52d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TMixClust_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TMixClust_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TMixClust_1.14.0.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: 29 Package: TNBC.CMS Version: 1.8.0 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: f5d5e6de69bd63c63e77f4edb9a8a1ee 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_13 git_last_commit: 8beeb75 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TNBC.CMS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TNBC.CMS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TNBC.CMS_1.8.0.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: 1.14.0 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: 27b57c154617036ca03100b0cb01af2d 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_13 git_last_commit: 87de662 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TnT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TnT_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TnT_1.14.0.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: 36 Package: TOAST Version: 1.6.0 Depends: R (>= 3.6), RefFreeEWAS, EpiDISH, limma, nnls Imports: stats, methods, SummarizedExperiment, corpcor Suggests: BiocStyle, knitr, rmarkdown, csSAM, gplots, matrixStats, Matrix License: GPL-2 MD5sum: d30deeef3d4cde48ad37ad1539a9143d 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. improve variable selection in reference-free deconvolution 3. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li 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_13 git_last_commit: 68b0442 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TOAST_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TOAST_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TOAST_1.6.0.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: 42 Package: tofsims Version: 1.20.0 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 MD5sum: dac5083f3e8ff783fe5a715bd339e8c8 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 git_url: https://git.bioconductor.org/packages/tofsims git_branch: RELEASE_3_13 git_last_commit: 681d0c3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tofsims_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tofsims_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tofsims_1.20.0.tgz vignettes: vignettes/tofsims/inst/doc/workflow.html vignetteTitles: Workflow with the `tofsims` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R dependencyCount: 17 Package: tomoda Version: 1.2.0 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 Archs: i386, x64 MD5sum: 62633ef79b9d862a7c8fe9c6331df92a 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, 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_13 git_last_commit: fa482a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tomoda_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tomoda_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tomoda_1.2.0.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: 75 Package: topconfects Version: 1.8.0 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: 89f17c16a64ea94156d8c9c3f617f7aa 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_13 git_last_commit: 52bb4b8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/topconfects_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topconfects_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topconfects_1.8.0.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 dependencyCount: 40 Package: topdownr Version: 1.14.0 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 (>= 2.11.4) Suggests: topdownrdata (>= 0.2), knitr, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 26f545730bfc08f6b6490b15fb6cf6ea 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_13 git_last_commit: 8a127d3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/topdownr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topdownr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topdownr_1.14.0.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: 84 Package: topGO Version: 2.44.0 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: 3e3235cb32fff58fa9a5ed0d8903055d 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_13 git_last_commit: d907c12 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/topGO_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topGO_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topGO_2.44.0.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, ideal, moanin, tRanslatome, ccTutorial, maEndToEnd importsMe: BioMM, cellity, FoldGO, GOSim, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: FGNet, geva, IntramiRExploreR, miRNAtap, Ringo dependencyCount: 52 Package: ToxicoGx Version: 1.2.1 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, 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: 2ee0635486ebd97c3aa9ed9feaf8f061 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_13 git_last_commit: 04482fe git_last_commit_date: 2021-06-17 Date/Publication: 2021-06-20 source.ver: src/contrib/ToxicoGx_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ToxicoGx_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ToxicoGx_1.2.1.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: 123 Package: TPP Version: 3.20.1 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: c61595234dedf38e60f3157d44beb040 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_13 git_last_commit: 97ad451 git_last_commit_date: 2021-07-27 Date/Publication: 2021-07-27 source.ver: src/contrib/TPP_3.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TPP_3.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TPP_3.20.1.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: 89 Package: TPP2D Version: 1.8.0 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: 11ee1dbc8413fb3d1a7d28702922fe97 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_13 git_last_commit: 432c4fa git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TPP2D_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TPP2D_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TPP2D_1.8.0.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: 65 Package: tracktables Version: 1.26.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) Archs: i386, x64 MD5sum: cbc6ea115396ce138a0a8d84a5faa820 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_13 git_last_commit: bccca31 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tracktables_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tracktables_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tracktables_1.26.0.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: 41 Package: trackViewer Version: 1.28.1 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: 406152a2ef62139e7839963e6b9fa736 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_13 git_last_commit: 84fca6e git_last_commit_date: 2021-07-06 Date/Publication: 2021-07-08 source.ver: src/contrib/trackViewer_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/trackViewer_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/trackViewer_1.28.1.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 suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 150 Package: tradeSeq Version: 1.6.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, ggplot2, princurve, methods, monocle, igraph, S4Vectors, tibble, Matrix, viridis, matrixStats Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment License: MIT + file LICENSE MD5sum: 5c4f6d2c6e9afef1f0ba6ef0bf6f06da 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 [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_13 git_last_commit: 6710190 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tradeSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tradeSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tradeSeq_1.6.0.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: 109 Package: TrajectoryGeometry Version: 1.0.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: 43de8e9adabe30b16f29817945d43c7d 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_13 git_last_commit: 2e2323e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TrajectoryGeometry_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TrajectoryGeometry_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryGeometry_1.0.0.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: 55 Package: TrajectoryUtils Version: 1.0.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 05051a749946bb402c031371597c6bb7 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_13 git_last_commit: f78814d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TrajectoryUtils_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TrajectoryUtils_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryUtils_1.0.0.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 dependencyCount: 30 Package: transcriptogramer Version: 1.14.0 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: a652f875db1d0227fdf71f535cf773ae 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_13 git_last_commit: 33f0ca5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/transcriptogramer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transcriptogramer_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transcriptogramer_1.14.0.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: 1.20.0 Depends: methods, R (>= 3.3) 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: b2152dc22685cd063ff9b7ed2f5c15a2 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_13 git_last_commit: 812d92c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/transcriptR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transcriptR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transcriptR_1.20.0.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: 148 Package: transite Version: 1.10.0 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: ea23280426470cb1bcfbea8c6b93c785 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_13 git_last_commit: 84f24a9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/transite_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transite_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transite_1.10.0.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: 60 Package: tRanslatome Version: 1.30.0 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 MD5sum: 1dad048b4c6ea07161c2f35af39f1d25 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_13 git_last_commit: 256a394 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tRanslatome_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRanslatome_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRanslatome_1.30.0.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: 1.2.2 Imports: RCy3, KEGGREST, dplyr Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: a8519f00bd7341cd237916d8e7971b73 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: Java 11, Cytoscape 3.8.2, Cy3D >= 1.1.3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_13 git_last_commit: 724a2d6 git_last_commit_date: 2021-10-08 Date/Publication: 2021-10-10 source.ver: src/contrib/transomics2cytoscape_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/transomics2cytoscape_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/transomics2cytoscape_1.2.2.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: 78 Package: TransView Version: 1.36.0 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: fc3723b2e4ccf1275eda745fb3d76d6f 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_13 git_last_commit: f763b52 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TransView_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TransView_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TransView_1.36.0.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: 22 Package: TraRe Version: 1.0.0 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: c8060582c661d72d946b975abb477132 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_13 git_last_commit: 8a2e150 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TraRe_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TraRe_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TraRe_1.0.0.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: 83 Package: traseR Version: 1.22.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: ae7785ca8c1a23fccead6d36b4f6667a 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_13 git_last_commit: c0cd9a7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/traseR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/traseR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/traseR_1.22.0.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: 46 Package: Travel Version: 1.0.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, inline, parallel License: GPL-3 MD5sum: d276ed97abdb123ab96ad68b6b24f38b 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] 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_13 git_last_commit: 8b0cf33 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Travel_1.0.0.tar.gz vignettes: vignettes/Travel/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Travel/inst/doc/vignette.R dependencyCount: 3 Package: TreeAndLeaf Version: 1.4.2 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 MD5sum: cab5c0568fd07935a2d8eb5d56d057f6 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_13 git_last_commit: af156ef git_last_commit_date: 2021-08-28 Date/Publication: 2021-09-02 source.ver: src/contrib/TreeAndLeaf_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/TreeAndLeaf_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/TreeAndLeaf_1.4.2.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 dependencyCount: 17 Package: treeio Version: 1.16.2 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.3.0), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: 99e9cec901728976da8b52b8e0d9bf7d 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://yulab-smu.top/treedata-book/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_13 git_last_commit: 5d5bfb8 git_last_commit_date: 2021-08-17 Date/Publication: 2021-08-17 source.ver: src/contrib/treeio_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/treeio_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.1/treeio_1.16.2.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, RevGadgets suggestsMe: enrichplot, ggtreeExtra, rfaRm, idiogramFISH, nosoi, tidytree dependencyCount: 35 Package: treekoR Version: 1.0.0 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, magrittr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 Archs: i386, x64 MD5sum: e345e2be8cb2d46adf35c18c510ebf15 NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Proportions to Parent 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; measure the proportions to parent (proportions of cells each node in the tree relative to the number of cells belonging its parent node), in addition to the proportions to all (proportion of cells in each node relative to all cells); 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_13 git_last_commit: 2b645b7 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/treekoR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/treekoR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/treekoR_1.0.0.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: 87 Package: TreeSummarizedExperiment Version: 2.0.3 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: e78d84b15714144d6957183e8f124911 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_13 git_last_commit: 0874f51 git_last_commit_date: 2021-08-15 Date/Publication: 2021-08-17 source.ver: src/contrib/TreeSummarizedExperiment_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/TreeSummarizedExperiment_2.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/TreeSummarizedExperiment_2.0.3.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, miaViz, curatedMetagenomicData, microbiomeDataSets dependencyCount: 62 Package: trena Version: 1.14.0 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, vbsr, xgboost, BiocParallel, 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 Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Athaliana.TAIR.TAIR9 License: GPL-3 MD5sum: bdb8b28746c6f9f5f38cbbb58ca1566e 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 BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_13 git_last_commit: c848203 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/trena_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trena_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trena_1.14.0.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: 118 Package: Trendy Version: 1.14.0 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 Archs: i386, x64 MD5sum: 501ee72b868fe74939983c8fef664c03 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_13 git_last_commit: 64504c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Trendy_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Trendy_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Trendy_1.14.0.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: 80 Package: tricycle Version: 1.0.0 Depends: R (>= 4.1), SingleCellExperiment Imports: methods, circular, ggplot2, 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: b886c6facfd3b27523543455be9196ab 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_13 git_last_commit: 0586b67 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tricycle_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tricycle_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tricycle_1.0.0.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: 110 Package: trigger Version: 1.38.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 1b2e6d18713c70c29d4e61c58d451a90 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_13 git_last_commit: a0ef31f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/trigger_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trigger_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trigger_1.38.0.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: 3.30.0 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: 074e8d4b3438f7abf25125cdf36a56d7 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_13 git_last_commit: 0ed315f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/trio_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trio_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trio_3.30.0.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: 19 Package: triplex Version: 1.32.0 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 MD5sum: b96754f1a8c25004cfc53a4e290127f8 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_13 git_last_commit: 70fc004 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/triplex_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/triplex_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/triplex_1.32.0.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: 21 Package: tRNA Version: 1.10.0 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 MD5sum: 61a09c32ff15b8ffd22ed779f632c398 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_13 git_last_commit: 5338871 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tRNA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNA_1.10.0.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: 56 Package: tRNAdbImport Version: 1.10.0 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: 7e7286c76a76939ca16203df5d3f4c9e 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_13 git_last_commit: 82fa20a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tRNAdbImport_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNAdbImport_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNAdbImport_1.10.0.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: 65 Package: tRNAscanImport Version: 1.12.0 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: 30cb7f667019a47bda14e0ce9b8e3999 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_13 git_last_commit: cb59bfe git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tRNAscanImport_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNAscanImport_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNAscanImport_1.12.0.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: 2.24.0 Depends: R (>= 4.0.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 MD5sum: 43f1b076e0a49013f1d5a1da7de7464b 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_13 git_last_commit: 327bbb9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TRONCO_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TRONCO_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TRONCO_2.24.0.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: 52 Package: TSCAN Version: 1.30.0 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: 9ae81dcacbe8bb84d79f8d9ba646584a 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_13 git_last_commit: ff908e9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TSCAN_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TSCAN_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TSCAN_1.30.0.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, DIscBIO suggestsMe: condiments dependencyCount: 87 Package: tscR Version: 1.4.0 Depends: R (>= 4.0), 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: GPL (>=2) Archs: i386, x64 MD5sum: c74dbe0c7d3cf064c1a92a236d862e95 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: Miriam Riquelme-Pérez and Fernando Pérez-Sanz Maintainer: Pérez-Sanz, Fernando VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tscR git_branch: RELEASE_3_13 git_last_commit: b8f82b0 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tscR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tscR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tscR_1.4.0.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: 91 Package: tspair Version: 1.50.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 MD5sum: c7bc7631f9aabf7e2e5b65ca5d5f6fd5 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: RELEASE_3_13 git_last_commit: bf4fcb4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tspair_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tspair_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tspair_1.50.0.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: 7 Package: TSRchitect Version: 1.18.0 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 Archs: i386, x64 MD5sum: 7809a04854da19076aa9f02033f201de 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 git_url: https://git.bioconductor.org/packages/TSRchitect git_branch: RELEASE_3_13 git_last_commit: daeccc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TSRchitect_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TSRchitect_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TSRchitect_1.18.0.tgz vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf, vignettes/TSRchitect/inst/doc/TSRchitect.html, vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html vignetteTitles: TSRchitect User's Guide, TSRchitect vignette, TSRchitect User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R dependencyCount: 117 Package: ttgsea Version: 1.0.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 7bf4a9d487b77b6e51ff1d9b4089b039 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_13 git_last_commit: 9a72aa9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ttgsea_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ttgsea_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ttgsea_1.0.0.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 dependencyCount: 122 Package: TTMap Version: 1.14.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: 3d04397b3b4c7ffc7d419c8fcf1eabaf 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_13 git_last_commit: b8057a5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TTMap_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TTMap_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TTMap_1.14.0.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: 47 Package: TurboNorm Version: 1.40.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL MD5sum: ac182ea71fc7fc16c7b9d4a2b32299ed 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_13 git_last_commit: 707184a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TurboNorm_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TurboNorm_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TurboNorm_1.40.0.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: 18 Package: TVTB Version: 1.18.0 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: c4b2e05b56f106ec583d51c63bbd7d45 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_13 git_last_commit: 954f99a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TVTB_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/TVTB_1.18.0.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: 151 Package: tweeDEseq Version: 1.38.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) MD5sum: 2afd86b9c7cfc6579a95fe17b0f3d0ea 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_13 git_last_commit: 375f48a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tweeDEseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tweeDEseq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tweeDEseq_1.38.0.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: 25 Package: twilight Version: 1.68.0 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) MD5sum: 77c3577f871832c3c31ff250d2cb561a 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_13 git_last_commit: d6432d4 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/twilight_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/twilight_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/twilight_1.68.0.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: 9 Package: twoddpcr Version: 1.16.0 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: i386, x64 MD5sum: 52047bbdc8854425cd55674b8dd10481 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_13 git_last_commit: c997928 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/twoddpcr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/twoddpcr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/twoddpcr_1.16.0.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: tximeta Version: 1.10.0 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, edgeR, limma, devtools License: GPL-2 Archs: i386, x64 MD5sum: 346f9cae00b58bab74df29381fbfcad9 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_13 git_last_commit: da94a30 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tximeta_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tximeta_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tximeta_1.10.0.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: 122 Package: tximport Version: 1.20.0 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: 88df7ee17bf3a870f7910eb2ac07fd40 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_13 git_last_commit: 5215e43 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/tximport_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tximport_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tximport_1.20.0.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 suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TypeInfo Version: 1.58.0 Depends: methods Suggests: Biobase License: BSD MD5sum: ddddbb0fd2866329844356d1e34725ab 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_13 git_last_commit: 11a4340 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/TypeInfo_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TypeInfo_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TypeInfo_1.58.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: Ularcirc Version: 1.10.0 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, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, Sushi, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: a057dcd3ee9ded7aee8bf240816e780e 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_13 git_last_commit: 5661ddd git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Ularcirc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Ularcirc_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Ularcirc_1.10.0.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: 146 Package: UMI4Cats Version: 1.2.1 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 Archs: i386, x64 MD5sum: 26aa8c36df43a0f1949badc8ea775c68 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_13 git_last_commit: a8ed2ca git_last_commit_date: 2021-06-11 Date/Publication: 2021-06-13 source.ver: src/contrib/UMI4Cats_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/UMI4Cats_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/UMI4Cats_1.2.1.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: 154 Package: uncoverappLib Version: 1.2.0 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, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, BSgenome.Hsapiens.UCSC.hg19, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 89027c5c318d990998c8165991a0d810 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. 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_13 git_last_commit: eac9828 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/uncoverappLib_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/uncoverappLib_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/uncoverappLib_1.2.0.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: 181 Package: UNDO Version: 1.34.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 226ed69e2f5c2a19c49fb6dd399faf35 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_13 git_last_commit: 93cb5fb git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/UNDO_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/UNDO_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/UNDO_1.34.0.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: 11 Package: unifiedWMWqPCR Version: 1.28.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 2632782871c32d444a83a72a0752f507 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_13 git_last_commit: cce7e4c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/unifiedWMWqPCR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/unifiedWMWqPCR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/unifiedWMWqPCR_1.28.0.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: 22 Package: UniProt.ws Version: 2.32.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, rappdirs Suggests: RUnit, BiocStyle, knitr License: Artistic License 2.0 MD5sum: 6ce699dc31ac30b0d9ab75a99080f72b 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], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_13 git_last_commit: f289fbf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/UniProt.ws_2.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/UniProt.ws_2.32.0.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: 63 Package: Uniquorn Version: 2.12.0 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: 20300ca4e5a1d8ef0ae69c5026cb391a 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_13 git_last_commit: 689be91 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Uniquorn_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Uniquorn_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Uniquorn_2.12.0.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: 1.10.2 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: 141e459464905b21a47ed7a2e3bcf28b 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_13 git_last_commit: 556bc8f git_last_commit_date: 2021-08-03 Date/Publication: 2021-08-05 source.ver: src/contrib/universalmotif_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/universalmotif_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.1/universalmotif_1.10.2.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 dependencyCount: 54 Package: uSORT Version: 1.18.0 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 MD5sum: 2a584f7452b5139a982f6d041c0a75f1 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_13 git_last_commit: d2b1154 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/uSORT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/uSORT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/uSORT_1.18.0.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: 103 Package: VanillaICE Version: 1.54.0 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: 07d9c81cc6b6105092233aceb8dc98c1 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_13 git_last_commit: 5c4c822 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VanillaICE_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VanillaICE_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VanillaICE_1.54.0.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: 1.0.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 3b3ea890ff17e951ebad0ef9e5fb4e45 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_13 git_last_commit: aeff27c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VarCon_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VarCon_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VarCon_1.0.0.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: 96 Package: variancePartition Version: 1.22.0 Depends: R (>= 3.6.0), ggplot2, limma, BiocParallel, scales, Biobase, methods Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, iterators, splines, foreach, doParallel, colorRamps, gplots, progress, reshape2, lme4 (>= 1.1-10), grDevices, graphics, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL (>= 2) MD5sum: bad1236e782a3f1b02d7741284c8b1f3 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 E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr BugReports: https://github.com/GabrielHoffman/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_13 git_last_commit: 25d1f1e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/variancePartition_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/variancePartition_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/variancePartition_1.22.0.tgz vignettes: vignettes/variancePartition/inst/doc/theory_practice_random_effects.pdf, 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 vignetteTitles: 3) Theory and practice of random effects and REML, 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Frequently asked questions 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: 89 Package: VariantAnnotation Version: 1.38.0 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: 5edc290f5e26ae801388b6fd409c1981 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_13 git_last_commit: 1deefec git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VariantAnnotation_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantAnnotation_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantAnnotation_1.38.0.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, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector 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, MADSEQ, methyAnalysis, MMAPPR2, motifbreakR, musicatk, MutationalPatterns, scoreInvHap, SigsPack, SNPhood, systemPipeR, TitanCNA, tLOH, TVTB, Uniquorn, VCFArray, XCIR, YAPSA, COSMIC.67 suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CNVgears, CrispRVariants, GenomicRanges, GenomicScores, GWASTools, omicsPrint, podkat, RVS, SeqArray, splatter, supersigs, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD dependencyCount: 97 Package: VariantExperiment Version: 1.6.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, GDSArray (>= 1.3.0), DelayedDataFrame (>= 1.0.0) Imports: tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr License: GPL-3 MD5sum: 55b103e7b65ce4414cdb7df423eadded 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_13 git_last_commit: 41711c2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VariantExperiment_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantExperiment_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantExperiment_1.6.0.tgz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.html vignetteTitles: VariantExperiment-class, VariantExperiment-methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.R dependencyCount: 67 Package: VariantFiltering Version: 1.28.0 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 MD5sum: e73b363d0af57ee2b1a8ecbabf548a58 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_13 git_last_commit: a7b2d16 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VariantFiltering_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantFiltering_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantFiltering_1.28.0.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: 170 Package: VariantTools Version: 1.34.0 Depends: 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 Archs: i386, x64 MD5sum: 300357f19936789ef0d3d87ffb500ecc 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_13 git_last_commit: f5b011f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VariantTools_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantTools_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantTools_1.34.0.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: 98 Package: VaSP Version: 1.4.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, Sushi, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 7e056e5c5b09c42c9a4943b63c255df1 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. 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_13 git_last_commit: 634a71f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VaSP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VaSP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VaSP_1.4.0.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: 111 Package: vbmp Version: 1.60.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) Archs: i386, x64 MD5sum: 3f5f05e7e127e6da065db6d17302abf9 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_13 git_last_commit: 2eddf66 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vbmp_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vbmp_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vbmp_1.60.0.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: 1.8.0 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: 5412aad0db0e0f77fe300ffe1225e238 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_13 git_last_commit: 5de9e30 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VCFArray_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VCFArray_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VCFArray_1.8.0.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: 99 Package: VegaMC Version: 3.30.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: 0047a3fb7f831aafed61131e49fc7f94 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_13 git_last_commit: 37e7f4f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VegaMC_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VegaMC_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VegaMC_3.30.0.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: 72 Package: velociraptor Version: 1.2.0 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 Archs: i386, x64 MD5sum: 45d17204b63aafcaccd58a8c9dbfb762 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_13 git_last_commit: 3e78025 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/velociraptor_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/velociraptor_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/velociraptor_1.2.0.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: 55 Package: VennDetail Version: 1.8.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 694403802e46de7374d64ad1c4e0c6ae 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_13 git_last_commit: 00c2f83 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VennDetail_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VennDetail_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VennDetail_1.8.0.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: 51 Package: VERSO Version: 1.2.0 Depends: R (>= 4.0.0) Imports: ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE Archs: i386, x64 MD5sum: 6edd27553671e141b7ff4be10e4f0a26 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 [ctb] 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_13 git_last_commit: db11dc9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/VERSO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VERSO_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VERSO_1.2.0.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: 1.12.0 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: b8fa2bed3631388b6f1edd42f7d3492d 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_13 git_last_commit: 8ce5d28 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vidger_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vidger_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vidger_1.12.0.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: 121 Package: viper Version: 1.26.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 7349a637881d471c2d4dd0990454900b 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_13 git_last_commit: 1c52a94 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/viper_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/viper_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/viper_1.26.0.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: decoupleR, diggit, RTN, diggitdata, dorothea suggestsMe: MethReg, MOMA dependencyCount: 21 Package: ViSEAGO Version: 1.6.0 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: 36d95fdbf0d74fedd51734da1867eb57 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_13 git_last_commit: 320ba38 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/ViSEAGO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ViSEAGO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ViSEAGO_1.6.0.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: 1.0.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, ggnewscale, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, Matrix, ggrepel, textstem Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, ggpubr, singscore, knitr, rmarkdown, prettydoc, BiocStyle License: GPL-3 MD5sum: 947dac3c5e624576ea7e5dec1d273366 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_13 git_last_commit: 1a90b74 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vissE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vissE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vissE_1.0.0.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: 155 Package: VplotR Version: 1.2.0 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 Archs: i386, x64 MD5sum: caef57339da8e6c34b0e316773f86165 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_13 git_last_commit: 8077df2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-21 source.ver: src/contrib/VplotR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VplotR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VplotR_1.2.0.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: 3.60.0 Depends: R (>= 3.4.0), Biobase Imports: methods, affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat License: Artistic-2.0 MD5sum: b23b7e61916cf5e798e2f04ab3a2198d NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, and works for 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_13 git_last_commit: 942f366 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vsn_3.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vsn_3.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vsn_3.60.0.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: affyPara, cellHTS2, webbioc, rnaseqGene importsMe: arrayQualityMetrics, bnem, coexnet, DAPAR, DEP, Doscheda, imageHTS, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca, Ringo, tilingArray, ExpressionNormalizationWorkflow suggestsMe: adSplit, beadarray, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, scp, twilight, estrogen, wrMisc dependencyCount: 47 Package: vtpnet Version: 0.32.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 193a32fd157326da32460b10b670e586 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_13 git_last_commit: adfd187 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vtpnet_0.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vtpnet_0.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vtpnet_0.32.0.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: 1.14.0 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 MD5sum: 794c1da0188b629a46964c10cc5be973 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_13 git_last_commit: e43bb90 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/vulcan_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vulcan_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vulcan_1.14.0.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: 206 Package: waddR Version: 1.6.1 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: 7279870445a17ff7030829682e0ba941 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_13 git_last_commit: 90d967f git_last_commit_date: 2021-05-28 Date/Publication: 2021-05-30 source.ver: src/contrib/waddR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/waddR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/waddR_1.6.1.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: 102 Package: wateRmelon Version: 1.36.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest, irlba Enhances: minfi License: GPL-3 MD5sum: 8f9092141a5de3ebdaed06833a931fed NeedsCompilation: no Title: Illumina 450 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: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic, Tyler Gorrie-Stone, Louis El Khoury Maintainer: Leo git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_13 git_last_commit: 2ff511c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/wateRmelon_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wateRmelon_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wateRmelon_1.36.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: The \Rpackage{wateRmelon} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 166 Package: wavClusteR Version: 2.26.0 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: 1fa57fc721162313ff65ba6cb4e4675e 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_13 git_last_commit: 14989df git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/wavClusteR_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wavClusteR_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wavClusteR_2.26.0.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: 142 Package: weaver Version: 1.58.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 607b39d5db4270feaed5ddd7491df345 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_13 git_last_commit: 7c31039 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/weaver_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/weaver_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/weaver_1.58.0.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: 1.64.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: 09a76dc3e2aa948aea1a349e8f4e6560 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_13 git_last_commit: 69f5af6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/webbioc_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/webbioc_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/webbioc_1.64.0.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: 88 Package: weitrix Version: 1.4.0 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 Archs: i386, x64 MD5sum: 3705717e12b131ebb3c9b45de8c20e2d 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_13 git_last_commit: 1fe8dc8 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/weitrix_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/weitrix_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/weitrix_1.4.0.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: 82 Package: widgetTools Version: 1.70.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: d4df55be793cc71f451a876717d6da3e 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_13 git_last_commit: f09a56a git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/widgetTools_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/widgetTools_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/widgetTools_1.70.0.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: 1.16.0 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: a5075a419ae605f928ccc6e3e12e16ae 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_13 git_last_commit: e02f0d6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/wiggleplotr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wiggleplotr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wiggleplotr_1.16.0.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: 1.2.1 Depends: R (>= 4.0.0) 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 Archs: i386, x64 MD5sum: f459c74d146c10edb24bd44369132623 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_13 git_last_commit: 4ce33a4 git_last_commit_date: 2021-06-15 Date/Publication: 2021-06-17 source.ver: src/contrib/wpm_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/wpm_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/wpm_1.2.1.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: 134 Package: wppi Version: 1.0.0 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 License: MIT + file LICENSE MD5sum: 771fdabb212a131def687b5d4973ff56 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] (), 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_13 git_last_commit: 0d3ed1c git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/wppi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wppi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wppi_1.0.0.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: 64 Package: Wrench Version: 1.10.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: df5da273576d9eb1590e262c2fba900b 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_13 git_last_commit: 58313f9 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Wrench_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Wrench_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Wrench_1.10.0.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: XCIR Version: 1.6.0 Depends: methods Imports: stats, utils, data.table, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 MD5sum: c05c468697dd11078bf6b8fb8c4486b2 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/XCIR git_branch: RELEASE_3_13 git_last_commit: a1c5af3 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/XCIR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XCIR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XCIR_1.6.0.tgz vignettes: vignettes/XCIR/inst/doc/xcir_intro.html vignetteTitles: Introduction to XCIR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XCIR/inst/doc/xcir_intro.R dependencyCount: 117 Package: xcms Version: 3.14.1 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0), MSnbase (>= 2.17.7) Imports: mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.23.7), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges, SummarizedExperiment, MsCoreUtils 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 Enhances: Rgraphviz, rgl, XML License: GPL (>= 2) + file LICENSE MD5sum: 5600d074aed8cb25632f27e2c229eff9 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_13 git_last_commit: 71f6b4f git_last_commit_date: 2021-07-23 Date/Publication: 2021-07-27 source.ver: src/contrib/xcms_3.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/xcms_3.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/xcms_3.14.1.tgz vignettes: vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: 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/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, MetaClean importsMe: CAMERA, cliqueMS, cosmiq, Risa, specmine.datasets suggestsMe: CluMSID, MassSpecWavelet, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, RAMClustR, specmine dependencyCount: 93 Package: XDE Version: 2.38.0 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 MD5sum: e49bf080728b6793d8dc608732b0ef85 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_13 git_last_commit: 3e89cb6 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/XDE_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XDE_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XDE_2.38.0.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: 63 Package: Xeva Version: 1.8.0 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: 0196021da3c23912102ad14e8178d8ae 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_13 git_last_commit: f71d6a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/Xeva_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Xeva_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Xeva_1.8.0.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: 149 Package: XINA Version: 1.10.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 5c35ea3ee7acd8629014db641b82223d 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_13 git_last_commit: 1ddc683 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/XINA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XINA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XINA_1.10.0.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: 68 Package: xmapbridge Version: 1.50.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 68e6e8d222a80050ed5315ed81982003 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_13 git_last_commit: 711de11 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/xmapbridge_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/xmapbridge_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/xmapbridge_1.50.0.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: 1.0.2 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: ee496e5095a7a42f389de6b173b3eae7 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_13 git_last_commit: 9ef6642 git_last_commit_date: 2021-06-02 Date/Publication: 2021-06-03 source.ver: src/contrib/XNAString_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/XNAString_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/XNAString_1.0.2.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: 72 Package: XVector Version: 0.32.0 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: f85b750229a2074ae9d529f5e5f93501 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_13 git_last_commit: 300392d git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/XVector_0.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XVector_0.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XVector_0.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, 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: 11 Package: yamss Version: 1.18.0 Depends: R (>= 3.3.0), 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 MD5sum: 3e02f7709fe8dec6a5a2bad97ea6a11b 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_13 git_last_commit: 6536737 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/yamss_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/yamss_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/yamss_1.18.0.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: 48 Package: YAPSA Version: 1.18.0 Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, PMCMR, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 38fec893c00b903a8d3350f04bccd7f2 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, Lea Jopp-Saile, Carolin Andresen, Zuguang Gu and Matthias Schlesner Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_13 git_last_commit: 0ce81ea git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/YAPSA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/YAPSA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/YAPSA_1.18.0.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: 186 Package: yarn Version: 1.18.0 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 Archs: i386, x64 MD5sum: da06a4987ed26a3fc32fa973a07fb124 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_13 git_last_commit: a8b8b57 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/yarn_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/yarn_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/yarn_1.18.0.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 dependencyCount: 157 Package: zellkonverter Version: 1.2.1 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils Suggests: covr, spelling, testthat, knitr, rmarkdown, BiocStyle, scRNAseq, HDF5Array, rhdf5, BiocFileCache License: MIT + file LICENSE MD5sum: a44e95a62fceacc777e9cda9a0faa644 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_13 git_last_commit: a3c4f31 git_last_commit_date: 2021-06-22 Date/Publication: 2021-06-22 source.ver: src/contrib/zellkonverter_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/zellkonverter_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/zellkonverter_1.2.1.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: HDF5Array dependencyCount: 39 Package: zFPKM Version: 1.14.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr License: GPL-3 | file LICENSE MD5sum: a04cdac948178db82725281636ecc7a5 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_13 git_last_commit: 196cddf git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/zFPKM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zFPKM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zFPKM_1.14.0.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 importsMe: DGEobj.utils dependencyCount: 64 Package: zinbwave Version: 1.14.2 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: 2e3265ad36f46bcdcf4daf364cade7ac 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_13 git_last_commit: a2db5c9 git_last_commit_date: 2021-09-14 Date/Publication: 2021-09-16 source.ver: src/contrib/zinbwave_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/zinbwave_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/zinbwave_1.14.2.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: clusterExperiment, scBFA, singleCellTK, digitalDLSorteR suggestsMe: MAST, splatter dependencyCount: 72 Package: zlibbioc Version: 1.38.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 8ef59236c632a09393687935c99a3afd 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_13 git_last_commit: b80b55e git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/zlibbioc_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zlibbioc_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zlibbioc_1.38.0.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, MADSEQ, makecdfenv, NanoMethViz, oligo, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, csaw, diffHic, maftools, methylKit, mzR, Rfastp, Rhtslib, scPipe, seqTools, ShortRead, jackalope dependencyCount: 0 Package: BrainStars Version: 1.35.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 Archs: i386, x64 NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO git_url: https://git.bioconductor.org/packages/BrainStars git_branch: master git_last_commit: 7a87bab git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-09 win.binary.ver: bin/windows/contrib/4.1/BrainStars_1.35.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BrainStars_1.35.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChIPSeqSpike Version: 1.12.0 Depends: R (>= 3.5), rtracklayer (>= 1.37.6) Imports: tools, stringr, Rsamtools, GenomicRanges, IRanges, seqplots, ggplot2, LSD, corrplot, methods, stats, grDevices, graphics, utils, BiocGenerics, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 NeedsCompilation: no Title: ChIP-Seq data scaling according to spike-in control Description: Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to determine the binding sites of any protein of interest, such as transcription factors or histones with or without a specific modification, at a genome scale. The many steps of the protocol can introduce biases that make ChIP-Seq more qualitative than quantitative. For instance, it was shown that global histone modification differences are not caught by traditional downstream data normalization techniques. A case study reported no differences in histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle this problem, external spike-in control were used to keep track of technical biases between conditions. Exogenous DNA from a different non-closely related species was inserted during the protocol to infer scaling factors that enabled an accurate normalization, thus revealing the inhibitor effect. ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions. biocViews: ImmunoOncology, ChIPSeq, Sequencing, Normalization, Transcription, Coverage, DifferentialMethylation, Epigenetics, DataImport, HistoneModification Author: Nicolas Descostes Maintainer: Nicolas Descostes VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/ChIPSeqSpike git_branch: RELEASE_3_13 git_last_commit: 8b73542 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 win.binary.ver: bin/windows/contrib/4.1/ChIPSeqSpike_1.12.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: destiny Version: 3.6.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, 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: nbconvertR (>= 1.3.2), igraph, testthat, FNN, tidyr Enhances: rgl, SingleCellExperiment License: GPL 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, jupyter nbconvert (see nbconvertR’s INSTALL file) VignetteBuilder: nbconvertR BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: RELEASE_3_13 git_last_commit: c3cef14 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-27 win.binary.ver: bin/windows/contrib/4.1/destiny_3.6.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ENVISIONQuery Version: 1.40.0 Depends: rJava, XML, utils License: GPL-2 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day git_url: https://git.bioconductor.org/packages/ENVISIONQuery git_branch: RELEASE_3_13 git_last_commit: 1cb737f git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-27 win.binary.ver: bin/windows/contrib/4.1/ENVISIONQuery_1.40.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gramm4R Version: 1.5.0 Depends: R (>= 3.6.0) Imports: basicTrendline,investr,minerva,psych,grDevices, graphics, stats,DelayedArray,SummarizedExperiment,DMwR,phyloseq Suggests: knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: Generalized correlation analysis and model construction strategy for metabolome and microbiome Description: Generalized Correlation Analysis for Metabolome and Microbiome (GRaMM), for inter-correlation pairs discovery among metabolome and microbiome. biocViews: GraphAndNetwork,Microbiome Author: Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia Maintainer: Tianlu Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gramm4R git_branch: master git_last_commit: 960fa29 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-08 win.binary.ver: bin/windows/contrib/4.1/gramm4R_1.5.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gramm4R_1.5.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Onassis Version: 1.14.0 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 NeedsCompilation: no 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 Author: Eugenia Galeota Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Onassis git_branch: RELEASE_3_13 git_last_commit: e143c4b git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 win.binary.ver: bin/windows/contrib/4.1/Onassis_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Onassis_1.14.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SRGnet Version: 1.17.0 Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc Suggests: knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: SRGnet: An R package for studying synergistic response to gene mutations from transcriptomics data Description: We developed SRGnet to analyze synergistic regulatory mechanisms in transcriptome profiles that act to enhance the overall cell response to combination of mutations, drugs or environmental exposure. This package can be used to identify regulatory modules downstream of synergistic response genes, prioritize synergistic regulatory genes that may be potential intervention targets, and contextualize gene perturbation experiments. biocViews: Software, StatisticalMethod, Regression Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre] Maintainer: Isar Nassiri VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SRGnet git_branch: master git_last_commit: fde0f26 git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-08 win.binary.ver: bin/windows/contrib/4.1/SRGnet_1.17.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SRGnet_1.17.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: synapter Version: 2.16.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle License: GPL-2 Archs: i386, x64 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 and Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: RELEASE_3_13 git_last_commit: 93006a2 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 win.binary.ver: bin/windows/contrib/4.1/synapter_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/synapter_2.16.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affyQCReport Version: 1.70.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman PackageStatus: Deprecated Package: pcot2 Version: 1.60.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression, Network Author: Sarah Song, Mik Black Maintainer: Sarah Song PackageStatus: Deprecated Package: SAGx Version: 1.66.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison, GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html PackageStatus: Deprecated Package: AffyExpress Version: 1.58.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li PackageStatus: Deprecated Package: OutlierD Version: 1.56.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ PackageStatus: Deprecated Package: yaqcaffy Version: 1.52.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto PackageStatus: Deprecated Package: BiocCaseStudies Version: 1.54.0 Depends: tools, methods, utils, Biobase Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer PackageStatus: Deprecated Package: PCpheno Version: 1.54.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur PackageStatus: Deprecated Package: ArrayTools Version: 1.52.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li PackageStatus: Deprecated Package: RNAither Version: 2.40.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO, ImmunoOncology Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali PackageStatus: Deprecated Package: SSPA Version: 2.32.1 Depends: R (>= 2.12), methods Imports: graphics, stats, qvalue, lattice, limma Suggests: BiocStyle, knitr, rmarkdown, genefilter, edgeR, DESeq License: GPL (>= 2) Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: ImmunoOncology, GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html VignetteBuilder: knitr PackageStatus: Deprecated Package: GeneAnswers Version: 2.34.0 Depends: R (>= 3.0.0), igraph, KEGGREST, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng git_url: https://git.bioconductor.org/packages/GeneAnswers git_branch: RELEASE_3_12 git_last_commit: a310951 git_last_commit_date: 2021-02-21 Date/Publication: 2021-02-21 Package: eisa Version: 1.44.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi PackageStatus: Deprecated Package: ExpressionView Version: 1.44.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression, GO, KEGG Author: Andreas Luscher Maintainer: Gabor Csardi PackageStatus: Deprecated Package: rnaSeqMap Version: 2.50.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: ImmunoOncology, Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski PackageStatus: Deprecated Package: AnnotationFuncs Version: 1.42.0 Depends: R (>= 2.7.0), AnnotationDbi Imports: DBI Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions PackageStatus: Deprecated Package: genoset Version: 1.48.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.27.3), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: testthat, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Title: A RangedSummarizedExperiment with methods for copy number analysis Description: GenoSet provides an extension of the RangedSummarizedExperiment class with additional API features. This class provides convenient and fast methods for working with segmented genomic data. Additionally, GenoSet provides the class RleDataFrame which stores runs of data along the genome for multiple samples and provides very fast summaries of arbitrary row sets (regions of the genome). biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset VignetteBuilder: knitr PackageStatus: Deprecated Package: RchyOptimyx Version: 2.32.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour PackageStatus: Deprecated Package: CancerMutationAnalysis Version: 1.34.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca PackageStatus: Deprecated Package: KEGGprofile Version: 1.34.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt,RCurl,ggplot2,reshape2 License: GPL (>= 2) Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao Package: DBChIP Version: 1.36.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang PackageStatus: Deprecated Package: EasyqpcR Version: 1.34.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain PackageStatus: Deprecated Package: bigmemoryExtras Version: 1.40.0 Depends: R (>= 2.12), bigmemory (>= 4.5.31) Imports: methods Suggests: testthat, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix Title: An extension of the bigmemory package with added safety, convenience, and a factor class Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras VignetteBuilder: knitr PackageStatus: Deprecated Package: dexus Version: 1.32.0 Depends: R (>= 2.15), methods, BiocGenerics Imports: stats Suggests: parallel, statmod, DESeq, RColorBrewer License: LGPL (>= 2.0) Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer PackageStatus: Deprecated Package: RDAVIDWebService Version: 1.30.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ PackageStatus: Deprecated Package: CexoR Version: 1.30.1 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE 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 (package 'skellam') is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate (IDR) for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal PackageStatus: Deprecated Package: ELBOW Version: 1.28.0 Depends: R (>= 2.15.0) Imports: graphics, stats, utils Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer, hgu133plus2cdf, hgu133plus2probe License: file LICENSE License_is_FOSS: yes License_restricts_use: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: ImmunoOncology, Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang PackageStatus: Deprecated Package: EDDA Version: 1.30.0 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) Title: Experimental Design in Differential Abundance analysis Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527). biocViews: ImmunoOncology, Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527 PackageStatus: Deprecated Package: Polyfit Version: 1.26.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden PackageStatus: Deprecated Package: seqplots Version: 1.30.0 Depends: R (>= 3.2.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.13.0), DBI, RSQLite, plotrix, fields, grid, kohonen, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT (>= 0.1.0), RColorBrewer, Rsamtools, GenomicAlignments, BiocManager Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ImmunoOncology, ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues PackageStatus: Deprecated Package: simulatorZ Version: 1.26.0 Depends: R (>= 3.5), Biobase, SummarizedExperiment, survival, CoxBoost, BiocGenerics Imports: graphics, stats, gbm, Hmisc, GenomicRanges, methods Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE License: Artistic-2.0 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSet and RangedSummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ BugReports: https://github.com/zhangyuqing/simulatorZ PackageStatus: Deprecated Package: ToPASeq Version: 1.26.0 Depends: R(>= 3.5.0), graphite Imports: Rcpp, graph, methods, Biobase, RBGL, SummarizedExperiment, gRbase, limma, corpcor LinkingTo: Rcpp Suggests: BiocStyle, airway, knitr, rmarkdown, DESeq2, DESeq, edgeR, plotrix, breastCancerVDX, EnrichmentBrowser License: AGPL-3 Title: Topology-based pathway analysis of RNA-seq data Description: Implementation of methods for topology-based pathway analysis of RNA-seq data. This includes Topological Analysis of Pathway Phenotype Association (TAPPA; Gao and Wang, 2007), PathWay Enrichment Analysis (PWEA; Hung et al., 2010), and the Pathway Regulation Score (PRS; Ibrahim et al., 2012). biocViews: ImmunoOncology, GeneExpression, RNASeq, DifferentialExpression, GraphAndNetwork, Pathways, NetworkEnrichment, Visualization Author: Ivana Ihnatova, Eva Budinska, Ludwig Geistlinger Maintainer: Ivana Ihnatova VignetteBuilder: knitr PackageStatus: Deprecated Package: mdgsa Version: 1.24.0 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL Title: Multi Dimensional Gene Set Analysis. Description: Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs. biocViews: GeneSetEnrichment, Annotation, Pathways, GO Author: David Montaner Maintainer: David Montaner URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com VignetteBuilder: knitr PackageStatus: Deprecated Package: RNAprobR Version: 1.24.0 Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1), BiocGenerics(>= 0.10.0) Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), IRanges(>= 2.10.5), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12), S4Vectors(>= 0.14.7), graphics, stats, utils Suggests: BiocStyle License: GPL (>=2) Title: An R package for analysis of massive parallel sequencing based RNA structure probing data Description: This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file. biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation Author: Lukasz Jan Kielpinski , Nikos Sidiropoulos , Jeppe Vinther Maintainer: Nikos Sidiropoulos PackageStatus: Deprecated Package: ENCODExplorer Version: 2.18.0 Depends: R (>= 3.6) Imports: methods, tools, jsonlite, RCurl, tidyr, data.table, dplyr, stringr, stringi, utils, AnnotationHub, GenomicRanges, rtracklayer, S4Vectors, GenomeInfoDb, ENCODExplorerData Suggests: RUnit,BiocGenerics,knitr, curl, httr, shiny, shinythemes, DT License: Artistic-2.0 Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant , Audrey Lemacon , Eric Fournier , Louis Gendron , Astrid-Louise Deschenes , Arnaud Droit Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues Package: XBSeq Version: 1.24.0 Depends: DESeq2, R (>= 3.3) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, magrittr, roar Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) Title: Test for differential expression for RNA-seq data Description: We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Sequencing, Software, ExperimentalDesign Author: Yuanhang Liu Maintainer: Yuanhang Liu URL: https://github.com/Liuy12/XBSeq VignetteBuilder: knitr PackageStatus: Deprecated Package: Imetagene Version: 1.22.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE NeedsCompilation: no Title: A graphical interface for the metagene package Description: This package provide a graphical user interface to the metagene package. This will allow people with minimal R experience to easily complete metagene analysis. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Audrey Lemacon , Charles Joly Beauparlant , Arnaud Droit Maintainer: Audrey Lemacon VignetteBuilder: knitr BugReports: https://github.com/andronekomimi/Imetagene/issues PackageStatus: Deprecated Package: metagenomeFeatures Version: 2.12.0 Depends: R (>= 3.5), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), S4Vectors (>= 0.23.18), dplyr (>= 0.7.0), dbplyr(>= 1.0.0), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>= 3.3.1), lattice (>= 0.20.33), ape (>= 3.5), DECIPHER (>= 2.4.0) Suggests: knitr (>= 1.11), testthat (>= 0.10.0), rmarkdown (>= 1.3), devtools (>= 1.13.5), ggtree(>= 1.8.2), BiocStyle (>= 2.8.2), phyloseq (>= 1.24.2), forcats (>= 0.3.0), ggplot2 (>= 3.0.0) License: Artistic-2.0 NeedsCompilation: no Title: Exploration of marker-gene sequence taxonomic annotations Description: metagenomeFeatures was developed for use in exploring the taxonomic annotations for a marker-gene metagenomic sequence dataset. The package can be used to explore the taxonomic composition of a marker-gene database or annotated sequences from a marker-gene metagenome experiment. biocViews: ImmunoOncology, Microbiome, Metagenomics, Annotation, Infrastructure, Sequencing, Software Author: Nathan D. Olson, Joseph Nathaniel Paulson, Hector Corrada Bravo Maintainer: Nathan D. Olson URL: https://github.com/HCBravoLab/metagenomeFeatures VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/metagenomeFeatures/issues PackageStatus: Deprecated Package: samExploreR Version: 1.16.0 Depends: ggplot2,Rsubread,RNAseqData.HNRNPC.bam.chr14,edgeR,R (>= 3.4.0) Imports: grDevices, stats, graphics Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL-3 Title: samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation Description: This R package is designed for subsampling procedure to simulate sequencing experiments with reduced sequencing depth. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA, HiSeq, MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. Was developed with usage of Rsubread. biocViews: ImmunoOncology, Sequencing, SequenceMatching, RNASeq, ChIPSeq, DNASeq, WholeGenome, GeneTarget, Alignment, GeneExpression, GeneticVariability, GeneRegulation, Preprocessing, GenomeAnnotation, Software Author: Alexey Stupnikov, Shailesh Tripathi and Frank Emmert-Streib Maintainer: shailesh tripathi PackageStatus: Deprecated Package: POST Version: 1.16.0 Depends: R (>= 3.4.0) Imports: stats, CompQuadForm, Matrix, survival, Biobase, GSEABase License: GPL (>= 2) Title: Projection onto Orthogonal Space Testing for High Dimensional Data Description: Perform orthogonal projection of high dimensional data of a set, and statistical modeling of phenotye with projected vectors as predictor. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao PackageStatus: Deprecated Package: cytofast Version: 1.8.0 Depends: R (>= 3.6.0) Imports: flowCore, ggplot2, ggridges, RColorBrewer, reshape2, stats, grDevices, Rdpack, methods, grid, FlowSOM Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Title: cytofast - A quick visualization and analysis tool for CyTOF data Description: Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. Together with tools like FlowSOM and Cytosplore it is possible to identify novel cell types. By introducing cytofast we hope to offer a workflow for visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or other clinical outcomes. biocViews: FlowCytometry, Visualization, Clustering Author: K.A. Stam , G. Beyrend Maintainer: K.A. Stam VignetteBuilder: knitr PackageStatus: Deprecated Package: CoRegFlux Version: 1.8.0 Depends: R (>= 3.6) Imports: CoRegNet, sybil Suggests: glpkAPI, testthat, knitr, rmarkdown, digest, R.cache, ggplot2, plyr, igraph, methods, latex2exp, rBayesianOptimization License: GPL-3 Title: CoRegFlux Description: CoRegFlux aims at providing tools to integrate reverse engineered gene regulatory networks and gene-expression into metabolic models to improve prediction of phenotypes, both for metabolic engineering, through transcription factor or gene (TF) knock-out or overexpression in various conditions as well as to improve our understanding of the interactions and cell inner-working. biocViews: GeneRegulation,Network,SystemsBiology,GeneExpression,Transcription,GenePrediction Author: Pauline Trébulle, Daniel Trejo-Banos, Mohamed Elati Maintainer: Pauline Trébulle and Mohamed Elati SystemRequirements: GLPK (>= 4.42) VignetteBuilder: knitr PackageStatus: Deprecated Package: HCABrowser Version: 1.8.0 Depends: R (>= 3.6.0), dplyr, AnVIL Imports: utils, methods, tibble, BiocFileCache, googleAuthR, httr, jsonlite, readr, rlang Suggests: BiocStyle, knitr, rmarkdown, stringr, testthat License: Artistic-2.0 Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing, SingleCell Author: Daniel Van Twisk , Martin Morgan , Bioconductor Package Maintainer Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues PackageStatus: Deprecated Package: CrossICC Version: 1.6.0 Depends: R (>= 3.5), MASS Imports: data.table, methods, MergeMaid, ConsensusClusterPlus, limma, cluster, dplyr, Biobase, grDevices, stats, graphics, utils Suggests: rmarkdown, testthat, knitr, shiny, shinydashboard, shinyWidgets, shinycssloaders, DT, ggthemes, ggplot2, pheatmap, RColorBrewer, tibble, ggalluvial License: GPL-3 | file LICENSE Title: An Interactive Consensus Clustering Framework for Multi-platform Data Analysis Description: CrossICC utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering and it is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions for visualization and identifying subtypes of cancer. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes. biocViews: Software, GeneExpression, DifferentialExpression, GUI, GeneSetEnrichment, Classification, Clustering, FeatureExtraction, Survival, Microarray, RNASeq, BatchEffect, Normalization, Preprocessing, Visualization Author: Yu Sun , Qi Zhao Maintainer: Yu Sun VignetteBuilder: knitr PackageStatus: Deprecated Package: HCAExplorer Version: 1.6.0 Depends: R (>= 3.6.0), dplyr Imports: BiocFileCache, HCAMatrixBrowser, S4Vectors, LoomExperiment, vctrs, curl, httr, jsonlite, methods, plyr, readr, rlang, tibble, tidygraph, utils, xml2 Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing Author: Daniel Van Twisk , Martin Morgan , Bioconductor Package Maintainer Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues PackageStatus: Deprecated Package: HCAMatrixBrowser Version: 1.2.0 Depends: R (>= 4.0.0), AnVIL Imports: BiocFileCache, digest, dplyr, httr, jsonlite, Matrix, methods, progress, rlang, SingleCellExperiment, stats, utils Suggests: BiocStyle, knitr, HCABrowser, LoomExperiment (>= 1.5.3), readr License: Artistic-2.0 Title: Extract and manage matrix data from the Human Cell Atlas project Description: The HCAMatrixBrowser queries the HCA matrix endpoint to download expression data and returns a standard Bioconductor object. It uses the LoomExperiment package to serve matrix data that is downloaded as HDF5 loom format. biocViews: Infrastructure, DataRepresentation, Software Author: Marcel Ramos , Martin Morgan Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCAMatrixBrowser PackageStatus: Deprecated Package: MouseFM Version: 1.2.0 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 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 , Inken Wohlers , Hauke Busch Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues