gnu: Add r-mixomics.
* gnu/packages/bioconductor.scm (r-mixomics): New variable. Signed-off-by: Ricardo Wurmus <rekado@elephly.net>
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					@ -4932,3 +4932,45 @@ change point detection.")
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     "FlowSOM offers visualization options for cytometry data, by using
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					     "FlowSOM offers visualization options for cytometry data, by using
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self-organizing map clustering and minimal spanning trees.")
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					self-organizing map clustering and minimal spanning trees.")
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    (license license:gpl2+)))
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					    (license license:gpl2+)))
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					(define-public r-mixomics
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					  (package
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					    (name "r-mixomics")
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					    (version "6.8.0")
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					    (source
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					     (origin
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					       (method url-fetch)
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					       (uri (bioconductor-uri "mixOmics" version))
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					       (sha256
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					        (base32
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					         "1f08jx35amn3sfcmqb96mjxxsm6dnpzhff625z758x1992wj4zsk"))))
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					    (properties `((upstream-name . "mixOmics")))
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					    (build-system r-build-system)
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					    (propagated-inputs
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					     `(("r-corpcor" ,r-corpcor)
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					       ("r-dplyr" ,r-dplyr)
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					       ("r-ellipse" ,r-ellipse)
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					       ("r-ggplot2" ,r-ggplot2)
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					       ("r-gridextra" ,r-gridextra)
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					       ("r-igraph" ,r-igraph)
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					       ("r-lattice" ,r-lattice)
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					       ("r-mass" ,r-mass)
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					       ("r-matrixstats" ,r-matrixstats)
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					       ("r-rarpack" ,r-rarpack)
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					       ("r-rcolorbrewer" ,r-rcolorbrewer)
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					       ("r-reshape2" ,r-reshape2)
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					       ("r-tidyr" ,r-tidyr)))
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					    (home-page "http://www.mixOmics.org")
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					    (synopsis "Multivariate methods for exploration of biological datasets")
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					    (description
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					     "mixOmics offers a wide range of multivariate methods for the exploration
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					and integration of biological datasets with a particular focus on variable
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					selection.  The package proposes several sparse multivariate models we have
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					developed to identify the key variables that are highly correlated, and/or
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					explain the biological outcome of interest.  The data that can be analysed
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					with mixOmics may come from high throughput sequencing technologies, such as
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					omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but
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					also beyond the realm of omics (e.g.  spectral imaging).  The methods
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					implemented in mixOmics can also handle missing values without having to
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					delete entire rows with missing data.")
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					    (license license:gpl2+)))
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