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-rw-r--r--gnu/packages/bioconductor.scm46
1 files changed, 46 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index a6d29e29c4..406ed2fb14 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -8041,3 +8041,49 @@ Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow.")
"This package provides a collection of software tools for calculating
distance measures.")
(license license:artistic2.0)))
+
+(define-public r-pcatools
+ (package
+ (name "r-pcatools")
+ (version "2.0.0")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (bioconductor-uri "PCAtools" version))
+ (sha256
+ (base32
+ "0mnwqrhm1hmhzwrpidf6z207w1ycpm572snvpp5swlg6hnxq6bnc"))))
+ (properties `((upstream-name . "PCAtools")))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-beachmat" ,r-beachmat)
+ ("r-bh" ,r-bh)
+ ("r-biocparallel" ,r-biocparallel)
+ ("r-biocsingular" ,r-biocsingular)
+ ("r-cowplot" ,r-cowplot)
+ ("r-delayedarray" ,r-delayedarray)
+ ("r-delayedmatrixstats" ,r-delayedmatrixstats)
+ ("r-dqrng" ,r-dqrng)
+ ("r-ggplot2" ,r-ggplot2)
+ ("r-ggrepel" ,r-ggrepel)
+ ("r-lattice" ,r-lattice)
+ ("r-matrix" ,r-matrix)
+ ("r-rcpp" ,r-rcpp)
+ ("r-reshape2" ,r-reshape2)))
+ (native-inputs `(("r-knitr" ,r-knitr)))
+ (home-page "https://github.com/kevinblighe/PCAtools")
+ (synopsis "PCAtools: everything Principal Components Analysis")
+ (description
+ "@dfn{Principal Component Analysis} (PCA) extracts the fundamental
+structure of the data without the need to build any model to represent it.
+This \"summary\" of the data is arrived at through a process of reduction that
+can transform the large number of variables into a lesser number that are
+uncorrelated (i.e. the 'principal components'), while at the same time being
+capable of easy interpretation on the original data. PCAtools provides
+functions for data exploration via PCA, and allows the user to generate
+publication-ready figures. PCA is performed via @code{BiocSingular}; users
+can also identify an optimal number of principal components via different
+metrics, such as the elbow method and Horn's parallel analysis, which has
+relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high
+dimensional mass cytometry data.")
+ (license license:gpl3)))