chevreulProcess 1.0.0
chevreulProcessR 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")
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").
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.
chevreulProcessThe 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:
library("chevreulProcess")
# Load the data
data("small_example_dataset")R session information.
#> R version 4.5.0 RC (2025-04-04 r88126 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
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#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] chevreulProcess_1.0.0 scater_1.36.0
#> [3] ggplot2_3.5.2 scuttle_1.18.0
#> [5] SingleCellExperiment_1.30.0 SummarizedExperiment_1.38.0
#> [7] Biobase_2.68.0 GenomicRanges_1.60.0
#> [9] GenomeInfoDb_1.44.0 IRanges_2.42.0
#> [11] S4Vectors_0.46.0 BiocGenerics_0.54.0
#> [13] generics_0.1.3 MatrixGenerics_1.20.0
#> [15] matrixStats_1.5.0 BiocStyle_2.36.0
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_2.0.0 shape_1.4.6.1
#> [3] magrittr_2.0.3 ggbeeswarm_0.7.2
#> [5] GenomicFeatures_1.60.0 rmarkdown_2.29
#> [7] GlobalOptions_0.1.2 fs_1.6.6
#> [9] BiocIO_1.18.0 vctrs_0.6.5
#> [11] memoise_2.0.1 Rsamtools_2.24.0
#> [13] DelayedMatrixStats_1.30.0 RCurl_1.98-1.17
#> [15] htmltools_0.5.8.1 S4Arrays_1.8.0
#> [17] curl_6.2.2 BiocNeighbors_2.2.0
#> [19] SparseArray_1.8.0 sass_0.4.10
#> [21] bslib_0.9.0 cachem_1.1.0
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#> [25] igraph_2.1.4 lifecycle_1.0.4
#> [27] pkgconfig_2.0.3 rsvd_1.0.5
#> [29] Matrix_1.7-3 R6_2.6.1
#> [31] fastmap_1.2.0 GenomeInfoDbData_1.2.14
#> [33] digest_0.6.37 colorspace_2.1-1
#> [35] AnnotationDbi_1.70.0 dqrng_0.4.1
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#> [89] UCSC.utils_1.4.0 EnsDb.Hsapiens.v86_2.99.0
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#> [93] evaluate_1.0.3 codetools_0.2-20
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#> [105] blob_1.2.4 AnnotationFilter_1.32.0
#> [107] scran_1.36.0 sparseMatrixStats_1.20.0
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