chevreuldata 0.99.16
chevreuldataR is an open-source statistical environment which can be easily modified to enhance its functionality via packages. chevreuldata is a R package available via the Bioconductor repository for packages. R can be installed on any operating system from CRAN after which you can install chevreuldata by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("chevreuldata")
## Check that you have a valid Bioconductor installation
BiocManager::valid()chevreuldata is an ExperimentHub based data package containing smart-seq based scRNA-seq data as a SingleCellExperiment object from human retinal organoids. All included data is generated by the Cobrinik laboratory at Children’s Hospital Los Angeles.
chevreuldataWe hope that chevreuldata will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("chevreuldata")
#> To cite package 'chevreuldata' in publications use:
#>
#> Stachelek K (2024). _chevreuldata: Example data for the chevreul
#> package_. R package version 0.99.16,
#> <https://github.com/cobriniklab/chevreuldata>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {chevreuldata: Example data for the chevreul package},
#> author = {Kevin Stachelek},
#> year = {2024},
#> note = {R package version 0.99.16},
#> url = {https://github.com/cobriniklab/chevreuldata},
#> }chevreuldatalibrary("chevreuldata")To access data use helper functions as below
chevreul_sce <- chevreuldata::human_gene_transcript_sce()
#> see ?chevreuldata and browseVignettes('chevreuldata') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> loading from cacheData has been processed using the chevreul package. Expression information is available for both gene (main experiment) and transcript (alt experiment) features
mainExpName(chevreul_sce)
#> [1] "gene"altExpNames(chevreul_sce)
#> [1] "transcript"cell metadata includes organoid age Age, preparation method Prep.Method, and louvain clustering identities at multiple resolutions gene_snn_res.x.x
colData(chevreul_sce)
#> DataFrame with 883 rows and 49 columns
#> orig.ident nCount_gene nFeature_gene nCount_RNA nFeature_RNA
#> <character> <numeric> <integer> <numeric> <numeric>
#> ds20181001-0001 ds20181001 526209 1532 2384251 8908
#> ds20181001-0002 ds20181001 209036 1038 891703 6107
#> ds20181001-0003 ds20181001 470724 1696 1748316 9366
#> ds20181001-0004 ds20181001 780501 1723 2361597 8895
#> ds20181001-0005 ds20181001 406661 1235 1774651 7313
#> ... ... ... ... ... ...
#> ds20181001-1036 ds20181001 476671 1108 1921861 6284
#> ds20181001-1037 ds20181001 455445 998 1808767 5590
#> ds20181001-1038 ds20181001 223697 1036 869327 5795
#> ds20181001-1039 ds20181001 575075 1159 2110461 6610
#> ds20181001-1040 ds20181001 618681 1184 2627989 6826
#> sample_id sample_id_1 tissue_type Kit_ID
#> <character> <character> <character> <character>
#> ds20181001-0001 ds20181001-0001 ds20181001-0001 organoid 1A
#> ds20181001-0002 ds20181001-0002 ds20181001-0002 organoid 1A
#> ds20181001-0003 ds20181001-0003 ds20181001-0003 organoid 1A
#> ds20181001-0004 ds20181001-0004 ds20181001-0004 organoid 1A
#> ds20181001-0005 ds20181001-0005 ds20181001-0005 organoid 1A
#> ... ... ... ... ...
#> ds20181001-1036 ds20181001-1036 ds20181001-1036 organoid 3C
#> ds20181001-1037 ds20181001-1037 ds20181001-1037 organoid 3C
#> ds20181001-1038 ds20181001-1038 ds20181001-1038 organoid 3C
#> ds20181001-1039 ds20181001-1039 ds20181001-1039 organoid 3C
#> ds20181001-1040 ds20181001-1040 ds20181001-1040 organoid 3C
#> Kit_sample Seq_Number Tissue.Type Prep.Method Prep.Number
#> <numeric> <numeric> <character> <character> <character>
#> ds20181001-0001 1 3 Organoid Kuwahara 115
#> ds20181001-0002 2 3 Organoid Kuwahara 115
#> ds20181001-0003 3 3 Organoid Kuwahara 115
#> ds20181001-0004 4 3 Organoid Kuwahara 115
#> ds20181001-0005 5 3 Organoid Kuwahara 115
#> ... ... ... ... ... ...
#> ds20181001-1036 76 3 Organoid Zhong 17-7
#> ds20181001-1037 77 3 Organoid Zhong 17-7
#> ds20181001-1038 78 3 Organoid Zhong 17-7
#> ds20181001-1039 79 3 Organoid Zhong 17-7
#> ds20181001-1040 80 3 Organoid Zhong 17-7
#> Age Time.Group Poor_Read_Number Moderate_Alignment
#> <numeric> <numeric> <logical> <logical>
#> ds20181001-0001 56 1 NA NA
#> ds20181001-0002 56 1 NA NA
#> ds20181001-0003 56 1 NA NA
#> ds20181001-0004 56 1 NA NA
#> ds20181001-0005 56 1 NA NA
#> ... ... ... ... ...
#> ds20181001-1036 225 8 NA NA
#> ds20181001-1037 225 8 NA NA
#> ds20181001-1038 225 8 NA NA
#> ds20181001-1039 225 8 NA NA
#> ds20181001-1040 225 8 NA NA
#> Rod_Cells Possible_Rods Non_Photoreceptors Collection_Method
#> <logical> <logical> <logical> <character>
#> ds20181001-0001 NA NA NA FACS
#> ds20181001-0002 NA NA NA FACS
#> ds20181001-0003 NA NA NA FACS
#> ds20181001-0004 NA NA NA FACS
#> ds20181001-0005 NA NA NA FACS
#> ... ... ... ... ...
#> ds20181001-1036 NA NA NA FACS
#> ds20181001-1037 NA NA NA FACS
#> ds20181001-1038 NA NA NA FACS
#> ds20181001-1039 NA NA NA FACS
#> ds20181001-1040 NA NA NA FACS
#> Outliers VSX2_Outlier X9_Cluster_Green_Rods HR_Cluster_LB
#> <logical> <logical> <logical> <logical>
#> ds20181001-0001 NA NA NA NA
#> ds20181001-0002 NA NA NA NA
#> ds20181001-0003 NA NA NA NA
#> ds20181001-0004 NA NA NA NA
#> ds20181001-0005 NA NA NA NA
#> ... ... ... ... ...
#> ds20181001-1036 NA NA NA NA
#> ds20181001-1037 NA NA NA NA
#> ds20181001-1038 NA NA NA NA
#> ds20181001-1039 NA NA NA NA
#> ds20181001-1040 NA NA NA NA
#> HR_Cluster_Black HR_Cluster_LG HR_Cluster_DG HR_Cluster_Pink
#> <logical> <logical> <logical> <logical>
#> ds20181001-0001 NA NA NA NA
#> ds20181001-0002 NA NA NA NA
#> ds20181001-0003 NA NA NA NA
#> ds20181001-0004 NA NA NA NA
#> ds20181001-0005 NA NA NA NA
#> ... ... ... ... ...
#> ds20181001-1036 NA NA NA NA
#> ds20181001-1037 NA NA NA NA
#> ds20181001-1038 NA NA NA NA
#> ds20181001-1039 NA NA NA NA
#> ds20181001-1040 NA NA NA NA
#> Cluster_Color Fetal_Age Old_Seq_Number Old_Seq_Kit_ID
#> <logical> <logical> <logical> <logical>
#> ds20181001-0001 NA NA NA NA
#> ds20181001-0002 NA NA NA NA
#> ds20181001-0003 NA NA NA NA
#> ds20181001-0004 NA NA NA NA
#> ds20181001-0005 NA NA NA NA
#> ... ... ... ... ...
#> ds20181001-1036 NA NA NA NA
#> ds20181001-1037 NA NA NA NA
#> ds20181001-1038 NA NA NA NA
#> ds20181001-1039 NA NA NA NA
#> ds20181001-1040 NA NA NA NA
#> excluded_because batch names type
#> <character> <character> <character> <character>
#> ds20181001-0001 keep Kuwahara ds20181001-0001 PE
#> ds20181001-0002 keep Kuwahara ds20181001-0002 PE
#> ds20181001-0003 keep Kuwahara ds20181001-0003 PE
#> ds20181001-0004 keep Kuwahara ds20181001-0004 PE
#> ds20181001-0005 keep Kuwahara ds20181001-0005 PE
#> ... ... ... ... ...
#> ds20181001-1036 keep Zhong ds20181001-1036 PE
#> ds20181001-1037 keep Zhong ds20181001-1037 PE
#> ds20181001-1038 keep Zhong ds20181001-1038 PE
#> ds20181001-1039 keep Zhong ds20181001-1039 PE
#> ds20181001-1040 keep Zhong ds20181001-1040 PE
#> gene_snn_res.0.2 seurat_clusters gene_snn_res.0.4
#> <factor> <factor> <factor>
#> ds20181001-0001 0 2 2
#> ds20181001-0002 0 2 2
#> ds20181001-0003 3 12 5
#> ds20181001-0004 3 12 5
#> ds20181001-0005 0 2 2
#> ... ... ... ...
#> ds20181001-1036 1 0 4
#> ds20181001-1037 1 3 1
#> ds20181001-1038 1 0 4
#> ds20181001-1039 1 0 4
#> ds20181001-1040 1 3 1
#> gene_snn_res.0.6 gene_snn_res.0.8 gene_snn_res.1 read_count
#> <factor> <factor> <factor> <character>
#> ds20181001-0001 2 2 2 NA
#> ds20181001-0002 2 2 2 NA
#> ds20181001-0003 5 5 6 NA
#> ds20181001-0004 5 5 6 NA
#> ds20181001-0005 2 2 2 NA
#> ... ... ... ... ...
#> ds20181001-1036 4 4 4 NA
#> ds20181001-1037 0 4 4 NA
#> ds20181001-1038 4 4 4 NA
#> ds20181001-1039 4 4 4 NA
#> ds20181001-1040 0 0 0 NA
#> percent.mt nCount_transcript nFeature_transcript ident
#> <numeric> <numeric> <integer> <factor>
#> ds20181001-0001 0.175937 525534 4290 2
#> ds20181001-0002 0.375185 208914 2592 2
#> ds20181001-0003 0.396471 470708 4646 12
#> ds20181001-0004 0.496239 779109 4845 12
#> ds20181001-0005 0.244852 406723 3244 2
#> ... ... ... ... ...
#> ds20181001-1036 0.198974 474402 2722 0
#> ds20181001-1037 0.611019 454162 2485 3
#> ds20181001-1038 0.539674 222386 2257 0
#> ds20181001-1039 0.126259 572366 2867 0
#> ds20181001-1040 0.254418 615519 2910 3For more information on data generation consult Shayler et al. https://www.biorxiv.org/content/10.1101/2023.02.28.530247v1
The chevreuldata package (Stachelek, 2024) was made possible thanks to:
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("human_gene_transcript_sce.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("human_gene_transcript_sce.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-11-15 12:55:17 EST"Wallclock time spent generating the vignette.
#> Time difference of 1.949 minsR session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R Under development (unstable) (2024-10-21 r87258)
#> os Ubuntu 24.04.1 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2024-11-15
#> pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.29. 2024. URL: https://github.com/rstudio/rmarkdown.
[2] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.
[3] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.35.0. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.
[4] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/.
[5] K. Stachelek. chevreuldata: Example data for the chevreul package. R package version 0.99.16. 2024. URL: https://github.com/cobriniklab/chevreuldata.
[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.
[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2. 2021. DOI: 10.32614/CRAN.package.sessioninfo. URL: https://CRAN.R-project.org/package=sessioninfo.
[8] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.49. 2024. URL: https://yihui.org/knitr/.