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
library(chevreuldata)
chevreul_sce <- human_gene_transcript_sce()
chevreul_sce
#> class: SingleCellExperiment 
#> dim: 56267 794 
#> metadata(4): merge.info pca.info experiment markers
#> assays(3): reconstructed counts logcounts
#> rownames(56267): 5-8S-rRNA 5S-rRNA ... ZZEF1 ZZZ3
#> rowData names(1): rotation
#> colnames(794): hs20151130-SC1-26 hs20151130-SC1-28 ...
#>   20200312-DS-dissected-81 20200312-DS-dissected-83
#> colData names(33): batch Sequencing_Run ... gene_snn_res.0.8
#>   gene_snn_res.1
#> reducedDimNames(3): corrected TSNE UMAP
#> mainExpName: integrated
#> altExpNames(2): gene transcript

R session information.

#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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] chevreuldata_0.99.19        ExperimentHub_2.15.0       
#>  [3] AnnotationHub_3.15.0        BiocFileCache_2.15.0       
#>  [5] dbplyr_2.5.0                chevreulProcess_0.99.22    
#>  [7] scater_1.35.0               ggplot2_3.5.1              
#>  [9] scuttle_1.17.0              SingleCellExperiment_1.29.1
#> [11] SummarizedExperiment_1.37.0 Biobase_2.67.0             
#> [13] GenomicRanges_1.59.1        GenomeInfoDb_1.43.1        
#> [15] IRanges_2.41.1              S4Vectors_0.45.2           
#> [17] BiocGenerics_0.53.3         generics_0.1.3             
#> [19] MatrixGenerics_1.19.0       matrixStats_1.4.1          
#> [21] BiocStyle_2.35.0           
#> 
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#>   [3] magrittr_2.0.3            ggbeeswarm_0.7.2         
#>   [5] GenomicFeatures_1.59.1    rmarkdown_2.29           
#>   [7] GlobalOptions_0.1.2       fs_1.6.5                 
#>   [9] BiocIO_1.17.0             zlibbioc_1.53.0          
#>  [11] vctrs_0.6.5               memoise_2.0.1            
#>  [13] Rsamtools_2.23.0          DelayedMatrixStats_1.29.0
#>  [15] RCurl_1.98-1.16           htmltools_0.5.8.1        
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#>  [19] BiocNeighbors_2.1.0       SparseArray_1.7.2        
#>  [21] sass_0.4.9                bslib_0.8.0              
#>  [23] cachem_1.1.0              ResidualMatrix_1.17.0    
#>  [25] GenomicAlignments_1.43.0  igraph_2.1.1             
#>  [27] mime_0.12                 lifecycle_1.0.4          
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#>  [47] bit64_4.5.2               withr_3.0.2              
#>  [49] BiocParallel_1.41.0       viridis_0.6.5            
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#>  [73] XVector_0.47.0            BiocVersion_3.21.1       
#>  [75] stringr_1.5.1             ggrepel_0.9.6            
#>  [77] pillar_1.9.0              limma_3.63.2             
#>  [79] circlize_0.4.16           dplyr_1.1.4              
#>  [81] lattice_0.22-6            rtracklayer_1.67.0       
#>  [83] bit_4.5.0                 tidyselect_1.2.1         
#>  [85] locfit_1.5-9.10           Biostrings_2.75.1        
#>  [87] knitr_1.49                gridExtra_2.3            
#>  [89] bookdown_0.41             ProtGenerics_1.39.0      
#>  [91] edgeR_4.5.0               cmdfun_1.0.2             
#>  [93] xfun_0.49                 statmod_1.5.0            
#>  [95] stringi_1.8.4             UCSC.utils_1.3.0         
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#>  [99] yaml_2.3.10               evaluate_1.0.1           
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