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 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#> 
#> 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)
#> 
#> 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_3.5.2               scuttle_1.19.0             
#>  [5] SingleCellExperiment_1.31.0 SummarizedExperiment_1.39.0
#>  [7] Biobase_2.69.0              GenomicRanges_1.61.0       
#>  [9] GenomeInfoDb_1.45.4         IRanges_2.43.0             
#> [11] S4Vectors_0.47.0            BiocGenerics_0.55.0        
#> [13] generics_0.1.4              MatrixGenerics_1.21.0      
#> [15] matrixStats_1.5.0           BiocStyle_2.37.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3        jsonlite_2.0.0           
#>   [3] shape_1.4.6.1             magrittr_2.0.3           
#>   [5] ggbeeswarm_0.7.2          GenomicFeatures_1.61.3   
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#>  [11] BiocIO_1.19.0             vctrs_0.6.5              
#>  [13] memoise_2.0.1             Rsamtools_2.25.0         
#>  [15] DelayedMatrixStats_1.31.0 RCurl_1.98-1.17          
#>  [17] htmltools_0.5.8.1         S4Arrays_1.9.1           
#>  [19] curl_6.2.3                BiocNeighbors_2.3.1      
#>  [21] SparseArray_1.9.0         sass_0.4.10              
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#>  [29] pkgconfig_2.0.3           rsvd_1.0.5               
#>  [31] Matrix_1.7-3              R6_2.6.1                 
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#>  [41] httr_1.4.7                abind_1.4-8              
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#>  [51] bluster_1.19.0            tools_4.5.0              
#>  [53] vipor_0.4.7               beeswarm_0.4.0           
#>  [55] glue_1.8.0                restfulr_0.0.15          
#>  [57] batchelor_1.25.0          grid_4.5.0               
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#>  [79] locfit_1.5-9.12           Biostrings_2.77.1        
#>  [81] knitr_1.50                gridExtra_2.3            
#>  [83] bookdown_0.43             ProtGenerics_1.41.0      
#>  [85] edgeR_4.7.2               cmdfun_1.0.2             
#>  [87] xfun_0.52                 statmod_1.5.0            
#>  [89] stringi_1.8.7             UCSC.utils_1.5.0         
#>  [91] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.2           
#>  [93] yaml_2.3.10               evaluate_1.0.3           
#>  [95] codetools_0.2-20          tibble_3.2.1             
#>  [97] BiocManager_1.30.25       cli_3.6.5                
#>  [99] jquerylib_0.1.4           dichromat_2.0-0.1        
#> [101] Rcpp_1.0.14               png_0.1-8                
#> [103] XML_3.99-0.18             parallel_4.5.0           
#> [105] readr_2.1.5               blob_1.2.4               
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