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| author | zimoun <zimon.toutoune@gmail.com> | 2019-07-24 20:22:03 +0200 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2019-07-24 23:05:39 +0200 |
| commit | 1adb9cbc5e9e4c458bbf3b3bb89fcd36278d34e2 (patch) | |
| tree | 18762888d4e4bf155eec90b383a97e9dcb9dd7dc | |
| parent | 1502751b3220feda44e34c07c617086538ac1296 (diff) | |
| download | guix-1adb9cbc5e9e4c458bbf3b3bb89fcd36278d34e2.tar.gz | |
gnu: Add r-mixomics.
* gnu/packages/bioconductor.scm (r-mixomics): New variable. Signed-off-by: Ricardo Wurmus <rekado@elephly.net>
| -rw-r--r-- | gnu/packages/bioconductor.scm | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index ad48ccaebe..f63bfa4a1f 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -4932,3 +4932,45 @@ change point detection.") "FlowSOM offers visualization options for cytometry data, by using self-organizing map clustering and minimal spanning trees.") (license license:gpl2+))) + +(define-public r-mixomics + (package + (name "r-mixomics") + (version "6.8.0") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "mixOmics" version)) + (sha256 + (base32 + "1f08jx35amn3sfcmqb96mjxxsm6dnpzhff625z758x1992wj4zsk")))) + (properties `((upstream-name . "mixOmics"))) + (build-system r-build-system) + (propagated-inputs + `(("r-corpcor" ,r-corpcor) + ("r-dplyr" ,r-dplyr) + ("r-ellipse" ,r-ellipse) + ("r-ggplot2" ,r-ggplot2) + ("r-gridextra" ,r-gridextra) + ("r-igraph" ,r-igraph) + ("r-lattice" ,r-lattice) + ("r-mass" ,r-mass) + ("r-matrixstats" ,r-matrixstats) + ("r-rarpack" ,r-rarpack) + ("r-rcolorbrewer" ,r-rcolorbrewer) + ("r-reshape2" ,r-reshape2) + ("r-tidyr" ,r-tidyr))) + (home-page "http://www.mixOmics.org") + (synopsis "Multivariate methods for exploration of biological datasets") + (description + "mixOmics offers a wide range of multivariate methods for the exploration +and integration of biological datasets with a particular focus on variable +selection. The package proposes several sparse multivariate models we have +developed to identify the key variables that are highly correlated, and/or +explain the biological outcome of interest. The data that can be analysed +with mixOmics may come from high throughput sequencing technologies, such as +omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but +also beyond the realm of omics (e.g. spectral imaging). The methods +implemented in mixOmics can also handle missing values without having to +delete entire rows with missing data.") + (license license:gpl2+))) |