chevreuldata 0.99.22
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 0.99.22,
#> <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 0.99.22},
#> 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
#> loading from cache
#> Loading required namespace: BiocSingularData 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] "integrated"altExpNames(chevreul_sce)
#> [1] "gene" "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 794 rows and 33 columns
#> batch Sequencing_Run
#> <character> <character>
#> hs20151130-SC1-26 20151130-HS-C1-Hs 20151130-HS-C1-Hs
#> hs20151130-SC1-28 20151130-HS-C1-Hs 20151130-HS-C1-Hs
#> hs20151130-SC1-29 20151130-HS-C1-Hs 20151130-HS-C1-Hs
#> hs20151130-SC1-41 20151130-HS-C1-Hs 20151130-HS-C1-Hs
#> hs20151130-SC1-53 20151130-HS-C1-Hs 20151130-HS-C1-Hs
#> ... ... ...
#> 20200312-DS-dissected-78 20200312-DS-dissecte.. 20200312-DS-dissecte..
#> 20200312-DS-dissected-79 20200312-DS-dissecte.. 20200312-DS-dissecte..
#> 20200312-DS-dissected-80 20200312-DS-dissecte.. 20200312-DS-dissecte..
#> 20200312-DS-dissected-81 20200312-DS-dissecte.. 20200312-DS-dissecte..
#> 20200312-DS-dissected-83 20200312-DS-dissecte.. 20200312-DS-dissecte..
#> nCount_Gene nFeature_Gene nCount_transcript
#> <numeric> <numeric> <numeric>
#> hs20151130-SC1-26 788792 3084 788792
#> hs20151130-SC1-28 597724 2109 597724
#> hs20151130-SC1-29 557492 3116 557492
#> hs20151130-SC1-41 173551 1381 173551
#> hs20151130-SC1-53 394839 1813 394839
#> ... ... ... ...
#> 20200312-DS-dissected-78 8418799 13494 8418799
#> 20200312-DS-dissected-79 3620195 9842 3620195
#> 20200312-DS-dissected-80 2088577 7635 2088577
#> 20200312-DS-dissected-81 907918 7942 907918
#> 20200312-DS-dissected-83 2870483 11220 2870483
#> nFeature_transcript Sample_ID Fetal_Age
#> <numeric> <character> <numeric>
#> hs20151130-SC1-26 4439 NA 17
#> hs20151130-SC1-28 3022 NA 17
#> hs20151130-SC1-29 4378 NA 17
#> hs20151130-SC1-41 1641 NA 17
#> hs20151130-SC1-53 2836 NA 17
#> ... ... ... ...
#> 20200312-DS-dissected-78 34453 20200312-DS-dissecte.. 16
#> 20200312-DS-dissected-79 21163 20200312-DS-dissecte.. 16
#> 20200312-DS-dissected-80 16046 20200312-DS-dissecte.. 16
#> 20200312-DS-dissected-81 14447 20200312-DS-dissecte.. 16
#> 20200312-DS-dissected-83 24821 20200312-DS-dissecte.. 16
#> Collection_Group Retina Collection_Method
#> <numeric> <character> <character>
#> hs20151130-SC1-26 1 17_8 C1
#> hs20151130-SC1-28 1 17_8 C1
#> hs20151130-SC1-29 1 17_0 C1
#> hs20151130-SC1-41 1 17_8 C1
#> hs20151130-SC1-53 1 17_8 C1
#> ... ... ... ...
#> 20200312-DS-dissected-78 18 16_1 FACS
#> 20200312-DS-dissected-79 18 16_9 FACS
#> 20200312-DS-dissected-80 18 16_3 FACS
#> 20200312-DS-dissected-81 18 16_0 FACS
#> 20200312-DS-dissected-83 18 16_3 FACS
#> S.Score G2M.Score Phase percent.mt
#> <numeric> <numeric> <character> <numeric>
#> hs20151130-SC1-26 -0.1139030 0.0965684 G2M 3.825333
#> hs20151130-SC1-28 -0.0882956 -0.0774102 G1 1.495177
#> hs20151130-SC1-29 0.0130033 -0.0825102 S 0.340833
#> hs20151130-SC1-41 0.0581814 -0.0345965 S 6.298886
#> hs20151130-SC1-53 -0.0928946 0.0958001 G2M 1.921312
#> ... ... ... ... ...
#> 20200312-DS-dissected-78 -0.0630722 -0.01325330 G1 0.825358
#> 20200312-DS-dissected-79 0.1023802 -0.04216473 S 0.877506
#> 20200312-DS-dissected-80 -0.1593829 -0.01744812 G1 0.645136
#> 20200312-DS-dissected-81 -0.1169104 0.00254407 G2M 0.363801
#> 20200312-DS-dissected-83 -0.1367366 -0.05563077 G1 0.643866
#> cluster_names_Res_0.4 cluster_names_Res_1.6
#> <character> <character>
#> hs20151130-SC1-26 iPRP TR
#> hs20151130-SC1-28 iPRP TR
#> hs20151130-SC1-29 ER ER
#> hs20151130-SC1-41 iPRP TR
#> hs20151130-SC1-53 ER TR
#> ... ... ...
#> 20200312-DS-dissected-78 LM LM1
#> 20200312-DS-dissected-79 RPC/MG MG
#> 20200312-DS-dissected-80 LM LM2
#> 20200312-DS-dissected-81 ER ER
#> 20200312-DS-dissected-83 LM LM2
#> integrated_snn_res.0.2 integrated_snn_res.0.4
#> <factor> <factor>
#> hs20151130-SC1-26 1 2
#> hs20151130-SC1-28 1 2
#> hs20151130-SC1-29 1 1
#> hs20151130-SC1-41 1 2
#> hs20151130-SC1-53 1 1
#> ... ... ...
#> 20200312-DS-dissected-78 0 0
#> 20200312-DS-dissected-79 2 3
#> 20200312-DS-dissected-80 0 0
#> 20200312-DS-dissected-81 1 1
#> 20200312-DS-dissected-83 0 0
#> integrated_snn_res.0.6 integrated_snn_res.0.8
#> <factor> <factor>
#> hs20151130-SC1-26 3 3
#> hs20151130-SC1-28 3 3
#> hs20151130-SC1-29 2 2
#> hs20151130-SC1-41 3 3
#> hs20151130-SC1-53 2 3
#> ... ... ...
#> 20200312-DS-dissected-78 1 1
#> 20200312-DS-dissected-79 4 4
#> 20200312-DS-dissected-80 0 0
#> 20200312-DS-dissected-81 2 2
#> 20200312-DS-dissected-83 0 0
#> integrated_snn_res.1.2 integrated_snn_res.1.4
#> <factor> <factor>
#> hs20151130-SC1-26 5 4
#> hs20151130-SC1-28 5 4
#> hs20151130-SC1-29 0 0
#> hs20151130-SC1-41 5 4
#> hs20151130-SC1-53 5 4
#> ... ... ...
#> 20200312-DS-dissected-78 1 1
#> 20200312-DS-dissected-79 5 4
#> 20200312-DS-dissected-80 4 3
#> 20200312-DS-dissected-81 0 0
#> 20200312-DS-dissected-83 4 3
#> integrated_snn_res.1.6 integrated_snn_res.1.8
#> <factor> <factor>
#> hs20151130-SC1-26 8 9
#> hs20151130-SC1-28 8 9
#> hs20151130-SC1-29 0 7
#> hs20151130-SC1-41 8 9
#> hs20151130-SC1-53 8 9
#> ... ... ...
#> 20200312-DS-dissected-78 1 1
#> 20200312-DS-dissected-79 9 10
#> 20200312-DS-dissected-80 3 3
#> 20200312-DS-dissected-81 0 0
#> 20200312-DS-dissected-83 3 3
#> integrated_snn_res.2 integrated_snn_res.1 ident
#> <factor> <factor> <factor>
#> hs20151130-SC1-26 9 3 9
#> hs20151130-SC1-28 9 3 9
#> hs20151130-SC1-29 7 1 7
#> hs20151130-SC1-41 9 3 9
#> hs20151130-SC1-53 9 3 9
#> ... ... ... ...
#> 20200312-DS-dissected-78 1 0 1
#> 20200312-DS-dissected-79 10 4 10
#> 20200312-DS-dissected-80 3 5 3
#> 20200312-DS-dissected-81 0 1 0
#> 20200312-DS-dissected-83 3 5 3
#> gene_snn_res.0.2 gene_snn_res.0.4 gene_snn_res.0.6
#> <factor> <factor> <factor>
#> hs20151130-SC1-26 1 1 1
#> hs20151130-SC1-28 2 1 1
#> hs20151130-SC1-29 1 2 2
#> hs20151130-SC1-41 1 1 1
#> hs20151130-SC1-53 2 1 1
#> ... ... ... ...
#> 20200312-DS-dissected-78 1 2 2
#> 20200312-DS-dissected-79 3 3 4
#> 20200312-DS-dissected-80 1 2 2
#> 20200312-DS-dissected-81 2 1 1
#> 20200312-DS-dissected-83 1 2 2
#> gene_snn_res.0.8 gene_snn_res.1
#> <factor> <factor>
#> hs20151130-SC1-26 1 1
#> hs20151130-SC1-28 1 2
#> hs20151130-SC1-29 2 1
#> hs20151130-SC1-41 1 1
#> hs20151130-SC1-53 1 2
#> ... ... ...
#> 20200312-DS-dissected-78 2 1
#> 20200312-DS-dissected-79 4 5
#> 20200312-DS-dissected-80 2 1
#> 20200312-DS-dissected-81 1 2
#> 20200312-DS-dissected-83 2 1For 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] "2024-11-19 16:28:16 EST"Wallclock time spent generating the vignette.
#> Time difference of 25.657 secsR 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 2024-11-19
#> 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).
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[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 0.99.22. 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/.