1 Basics

1.1 Install chevreulProcess

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. chevreulProcess 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 chevreulProcess by using the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("chevreulProcess")

1.2 Required knowledge

The chevreulProcess 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 chevreulProcess include, Biocpkg("SummarizedExperiment") and Biocpkg("scater").

1.3 Asking for help

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 chevreulProcess tag and check the older posts.

2 Quick start to using chevreulProcess

The chevreulProcess 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:

  • Performing Clustering at a range of resolutions and Dimensional reduction of Raw Sequencing Data.
  • Visualizing scRNA data using different plotting functions.
  • Integration of multiple datasets for consistent analyses.
  • Cell cycle state regression and labeling.

library("chevreulProcess")

# Load the data
data("small_example_dataset")

R session information.

#> R version 4.5.1 Patched (2025-08-23 r88802)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
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#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
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#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] chevreulProcess_1.1.2       scater_1.37.0              
#>  [3] ggplot2_4.0.0               scuttle_1.19.0             
#>  [5] SingleCellExperiment_1.31.1 SummarizedExperiment_1.39.2
#>  [7] Biobase_2.69.1              GenomicRanges_1.61.5       
#>  [9] Seqinfo_0.99.2              IRanges_2.43.5             
#> [11] S4Vectors_0.47.4            BiocGenerics_0.55.1        
#> [13] generics_0.1.4              MatrixGenerics_1.21.0      
#> [15] matrixStats_1.5.0           BiocStyle_2.37.1           
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3        jsonlite_2.0.0           
#>   [3] shape_1.4.6.1             magrittr_2.0.4           
#>   [5] ggbeeswarm_0.7.2          GenomicFeatures_1.61.6   
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#>  [11] BiocIO_1.19.0             vctrs_0.6.5              
#>  [13] memoise_2.0.1             Rsamtools_2.25.3         
#>  [15] DelayedMatrixStats_1.31.0 RCurl_1.98-1.17          
#>  [17] htmltools_0.5.8.1         S4Arrays_1.9.1           
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#>  [39] RSQLite_2.4.3             beachmat_2.25.5          
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#>  [51] rjson_0.2.23              bluster_1.19.0           
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#>  [57] restfulr_0.0.16           batchelor_1.25.0         
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#>  [81] Biostrings_2.77.2         knitr_1.50               
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#>  [85] ProtGenerics_1.41.0       edgeR_4.7.5              
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#>  [91] UCSC.utils_1.5.0          EnsDb.Hsapiens.v86_2.99.0
#>  [93] lazyeval_0.2.2            yaml_2.3.10              
#>  [95] evaluate_1.0.5            codetools_0.2-20         
#>  [97] tibble_3.3.0              BiocManager_1.30.26      
#>  [99] cli_3.6.5                 jquerylib_0.1.4          
#> [101] dichromat_2.0-0.1         Rcpp_1.1.0               
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