Subject: [PATCH 11/11] gnu: Add r-depecher.

From: zimoun <[email-masked]>
Date: Wed, 24 Jul 2019 20:22:04 +0200

* gnu/packages/bioconductor.scm (r-depecher): New variable.
---
 gnu/packages/bioconductor.scm | 44 +++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index 64625aedd4..7c9a3b7417 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -5013,3 +5013,47 @@ 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+)))
+
+(define-public r-depecher
+  (package
+    (name "r-depecher")
+    (version "1.0.3")
+    (source
+      (origin
+        (method url-fetch)
+        (uri (bioconductor-uri "DepecheR" version))
+        (sha256
+          (base32
+            "0qj2h2a50fncppvi2phh0mbivxkn1mv702mqpi9mvvkf3bzq8m0h"))))
+    (properties `((upstream-name . "DepecheR")))
+    (build-system r-build-system)
+    (propagated-inputs
+      `(("r-beanplot" ,r-beanplot)
+        ("r-biocparallel" ,r-biocparallel)
+        ("r-dosnow" ,r-dosnow)
+        ("r-dplyr" ,r-dplyr)
+        ("r-foreach" ,r-foreach)
+        ("r-ggplot2" ,r-ggplot2)
+        ("r-gplots" ,r-gplots)
+        ("r-mass" ,r-mass)
+        ("r-matrixstats" ,r-matrixstats)
+        ("r-mixomics" ,r-mixomics)
+        ("r-moments" ,r-moments)
+        ("r-rcpp" ,r-rcpp)
+        ("r-rcppeigen" ,r-rcppeigen)
+        ("r-reshape2" ,r-reshape2)
+        ("r-viridis" ,r-viridis)))
+    (home-page
+      "https://bioconductor.org/packages/DepecheR")
+    (synopsis
+      "Determination of essential phenotypic elements of clusters in high-dimensional entities")
+    (description
+     "The purpose of this package is to identify traits in a dataset that can
+separate groups.  This is done on two levels.  First, clustering is performed,
+using an implementation of sparse K-means.  Secondly, the generated clusters
+are used to predict outcomes of groups of individuals based on their
+distribution of observations in the different clusters.  As certain clusters
+with separating information will be identified, and these clusters are defined
+by a sparse number of variables, this method can reduce the complexity of
+data, to only emphasize the data that actually matters.")
+    (license license:expat)))
-- 
2.21.0