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| -rw-r--r-- | gnu/packages/bioconductor.scm | 46 |
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))) |