* gnu/packages/bioconductor.scm (r-mixomics): New variable.--- gnu/packages/bioconductor.scm | 54 +++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+)

## Toggle diff (64 lines)

diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scmindex dd6570ed17..64625aedd4 100644--- a/gnu/packages/bioconductor.scm+++ b/gnu/packages/bioconductor.scm@@ -4959,3 +4959,57 @@ and to both short and long sequence reads.") "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 "Omics Data Integration Project")+ (description+ "Multivariate methods are well suited to large omics data sets where the+number of variables (e.g. genes, proteins, metabolites) is much larger than+the number of samples (patients, cells, mice). They have the appealing+properties of reducing the dimension of the data by using instrumental+variables (components), which are defined as combinations of all variables.+Those components are then used to produce useful graphical outputs that enable+better understanding of the relationships and correlation structures between+the different data sets that are integrated. 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.+A non exhaustive list of methods include variants of generalised Canonical+Correlation Analysis, sparse Partial Least Squares and sparse Discriminant+Analysis. Recently we implemented integrative methods to combine multiple+data sets: N-integration with variants of Generalised Canonical Correlation+Analysis and P-integration with variants of multi-group Partial Least+Squares.")+ (license license:gpl2+)))-- 2.21.0