# chevreulProcess

# Basics

## 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")

## 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")`.

## 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.

# 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 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [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: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] chevreulProcess_1.5.0       scater_1.41.1
    #>  [3] ggplot2_4.0.3               scuttle_1.23.0
    #>  [5] SingleCellExperiment_1.35.1 SummarizedExperiment_1.43.0
    #>  [7] Biobase_2.73.1              GenomicRanges_1.65.0
    #>  [9] Seqinfo_1.3.0               IRanges_2.47.0
    #> [11] S4Vectors_0.51.1            BiocGenerics_0.59.1
    #> [13] generics_0.1.4              MatrixGenerics_1.25.0
    #> [15] matrixStats_1.5.0           BiocStyle_2.41.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3        sys_3.4.3
    #>   [3] jsonlite_2.0.0            shape_1.4.6.1
    #>   [5] magrittr_2.0.5            ggbeeswarm_0.7.3
    #>   [7] GenomicFeatures_1.65.0    farver_2.1.2
    #>   [9] rmarkdown_2.31            GlobalOptions_0.1.4
    #>  [11] fs_2.1.0                  BiocIO_1.23.3
    #>  [13] vctrs_0.7.3               memoise_2.0.1
    #>  [15] Rsamtools_2.29.0          DelayedMatrixStats_1.35.0
    #>  [17] RCurl_1.98-1.18           htmltools_0.5.9
    #>  [19] S4Arrays_1.13.0           BiocBaseUtils_1.15.1
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    #>  [27] ResidualMatrix_1.23.0     buildtools_1.0.0
    #>  [29] GenomicAlignments_1.49.0  igraph_2.3.1
    #>  [31] lifecycle_1.0.5           pkgconfig_2.0.3
    #>  [33] rsvd_1.0.5                Matrix_1.7-5
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    #>  [37] digest_0.6.39             colorspace_2.1-2
    #>  [39] AnnotationDbi_1.75.0      dqrng_0.4.1
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    #>  [49] S7_0.2.2                  BiocParallel_1.47.0
    #>  [51] viridis_0.6.5             DBI_1.3.0
    #>  [53] DelayedArray_0.39.1       rjson_0.2.23
    #>  [55] bluster_1.23.0            tools_4.6.0
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    #>  [81] bit_4.6.0                 tidyselect_1.2.1
    #>  [83] locfit_1.5-9.12           maketools_1.3.2
    #>  [85] Biostrings_2.81.1         knitr_1.51
    #>  [87] gridExtra_2.3             ProtGenerics_1.45.0
    #>  [89] edgeR_4.11.0              cmdfun_1.0.2
    #>  [91] xfun_0.57                 statmod_1.5.1
    #>  [93] stringi_1.8.7             UCSC.utils_1.9.0
    #>  [95] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.3
    #>  [97] yaml_2.3.12               evaluate_1.0.5
    #>  [99] codetools_0.2-20          cigarillo_1.3.0
    #> [101] tibble_3.3.1              BiocManager_1.30.27
    #> [103] cli_3.6.6                 jquerylib_0.1.4
    #> [105] Rcpp_1.1.1-1.1            GenomeInfoDb_1.49.0
    #> [107] png_0.1-9                 XML_3.99-0.23
    #> [109] parallel_4.6.0            readr_2.2.0
    #> [111] blob_1.3.0                AnnotationFilter_1.37.0
    #> [113] scran_1.41.0              sparseMatrixStats_1.25.0
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