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: 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(49): orig.ident nCount_gene ... nFeature_transcript ident
#> reducedDimNames(2): PCA UMAP
#> mainExpName: gene
#> altExpNames(1): transcript

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