chevreulProcess 0.99.10
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")
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.
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:
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
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_0.99.16 ExperimentHub_2.15.0
#> [3] AnnotationHub_3.15.0 BiocFileCache_2.15.0
#> [5] dbplyr_2.5.0 chevreulProcess_0.99.10
#> [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
#>
#> loaded via a namespace (and not attached):
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#> [3] magrittr_2.0.3 ggbeeswarm_0.7.2
#> [5] GenomicFeatures_1.59.1 rmarkdown_2.29
#> [7] fs_1.6.5 GlobalOptions_0.1.2
#> [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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#> [25] GenomicAlignments_1.43.0 igraph_2.1.1
#> [27] mime_0.12 lifecycle_1.0.4
#> [29] pkgconfig_2.0.3 rsvd_1.0.5
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#> [87] locfit_1.5-9.10 Biostrings_2.75.1
#> [89] knitr_1.49 gridExtra_2.3
#> [91] bookdown_0.41 ProtGenerics_1.39.0
#> [93] edgeR_4.5.0 cmdfun_1.0.2
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#> [97] stringi_1.8.4 UCSC.utils_1.3.0
#> [99] EnsDb.Hsapiens.v86_2.99.0 lazyeval_0.2.2
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