chevreul 0.99.11
chevreulR is an open-source statistical environment which can be easily modified to enhance its functionality via packages. chevreul 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 chevreul by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("chevreul")
## Check that you have a valid Bioconductor installation
BiocManager::valid()The chevreul package is designed for single-cell RNA sequencing data. The functions included within this package are derived from other packages that have implemented the infrastructure needed for RNA-seq data processing and analysis. Packages that have been instrumental in the development of chevreul include, Biocpkg("SummarizedExperiment") and Biocpkg("scater").
R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The Bioconductor support site is the main resource for getting help: remember to use the chevreul tag and check the older posts.
chevreulWe hope that chevreul will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("chevreul")
#> To cite package 'chevreul' in publications use:
#>
#> Stachelek K, Bhat B (2024). _chevreul: Tools for managing
#> SingleCellExperiment objects as projects_. R package version 0.99.11,
#> https://whtns.github.io/chevreul/,
#> <https://github.com/whtns/chevreul>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {chevreul: Tools for managing SingleCellExperiment objects as projects},
#> author = {Kevin Stachelek and Bhavana Bhat},
#> year = {2024},
#> note = {R package version 0.99.11,
#> https://whtns.github.io/chevreul/},
#> url = {https://github.com/whtns/chevreul},
#> }Here is an example of you can cite your package inside the vignette:
chevreulThe chevreul package contains functions to preprocess, cluster, visualize, and perform other analyses on scRNA-seq data. It also contains a shiny app for easy visualization and analysis of scRNA data.
chvereul uses SingelCellExperiment (SCE) object type (from SingleCellExperiment) to store expression and other metadata from single-cell experiments.
This package features functions capable of:
library("chevreul")
# Load the data
chevreul_sce <- chevreuldata::human_gene_transcript_sce()
chevreul_sce
#> class: SingleCellExperiment
#> dim: 9740 883
#> metadata(2): markers experiment
#> assays(3): counts logcounts scaledata
#> rownames(9740): 5-8S-rRNA A2M-AS1 ... HHIP-AS1 AC117490.2
#> rowData names(0):
#> colnames(883): ds20181001-0001 ds20181001-0002 ... ds20181001-1039
#> ds20181001-1040
#> colData names(64): orig.ident nCount_gene ... transcript_snn_res.2
#> ident
#> reducedDimNames(2): PCA UMAP
#> mainExpName: gene
#> altExpNames(1): transcriptWe start with a count matrix containing expression count data and a table of corresponding cell metadata.
# Object count matrix
human_count <- counts(chevreul_sce)
human_count[1:5, 1:5] %>% as.matrix() %>% knitr::kable(format = "pipe")| ds20181001-0001 | ds20181001-0002 | ds20181001-0003 | ds20181001-0004 | ds20181001-0005 | |
|---|---|---|---|---|---|
| 5-8S-rRNA | 0.00000 | 0.0000 | 0.0000 | 0.0000000 | 0.00000 |
| A2M-AS1 | 0.00000 | 0.0000 | 0.0000 | 0.0000000 | 0.00000 |
| A4GNT | 0.00000 | 0.0000 | 0.0000 | 0.0000000 | 0.00000 |
| AADACL2-AS1 | 0.00000 | 0.0000 | 0.0000 | 0.0000000 | 0.00000 |
| AAK1 | 67.89652 | 25.6852 | 182.6446 | 0.0627317 | 64.49659 |
# Object metadata
human_meta <- colData(chevreul_sce) %>% as.data.frame()
human_meta[1:5, 1:5] %>% as.matrix() %>% knitr::kable(format = "pipe")| orig.ident | nCount_gene | nFeature_gene | nCount_RNA | nFeature_RNA | |
|---|---|---|---|---|---|
| ds20181001-0001 | ds20181001 | 526209.4 | 1532 | 2384251.4 | 8908 |
| ds20181001-0002 | ds20181001 | 209036.3 | 1038 | 891702.8 | 6107 |
| ds20181001-0003 | ds20181001 | 470723.7 | 1696 | 1748316.1 | 9366 |
| ds20181001-0004 | ds20181001 | 780500.9 | 1723 | 2361597.0 | 8895 |
| ds20181001-0005 | ds20181001 | 406661.3 | 1235 | 1774650.6 | 7313 |
Then using these 2 datasets, we create a SingleCellExperiment object as shown below.
myobject <- SingleCellExperiment::SingleCellExperiment(
assays = list(counts = counts(chevreul_sce)),
colData = colData(chevreul_sce),
mainExpName = "gene"
)
myobject
#> class: SingleCellExperiment
#> dim: 9740 883
#> metadata(0):
#> assays(1): counts
#> rownames(9740): 5-8S-rRNA A2M-AS1 ... HHIP-AS1 AC117490.2
#> rowData names(0):
#> colnames(883): ds20181001-0001 ds20181001-0002 ... ds20181001-1039
#> ds20181001-1040
#> colData names(64): orig.ident nCount_gene ... transcript_snn_res.2
#> ident
#> reducedDimNames(0):
#> mainExpName: gene
#> altExpNames(0):Chevreul includes a shiny app for exploratory scRNA data analysis and visualization which can be accessed via
minimalSceApp(chevreul_sce)
Note: the SCE object must be pre-processed and integrated (if required) prior to building the shiny app.
The app is arranged into different sections each of which performs different function. More information about individual sections of the app is provided within the “shiny app” vignette.
The chevreul package (Stachelek and Bhat, 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("chevreul.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("chevreul.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-08-11 15:29:12 EDT"Wallclock time spent generating the vignette.
#> Time difference of 27.264 secsR session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.1 (2024-06-14)
#> os Ubuntu 22.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2024-08-11
#> pandoc 2.7.3 @ /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 2.27. 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 2.33.1. 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 and B. Bhat. chevreul: Tools for managing SingleCellExperiment objects as projects. R package version 0.99.11, https://whtns.github.io/chevreul/. 2024. URL: https://github.com/whtns/chevreul.
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[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2. 2021. 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 1.48. 2024. URL: https://yihui.org/knitr/.