diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index 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+)))