[PATCH] gnu: Add r-sva

[Ra <ilpuccio.febo@gmail.com>]

[-- Attachment #1.1: Type: text/plain, Size: 2331 bytes --]

* gnu/packages/bioinformatics.scm (r-sva): New variable.
---
 gnu/packages/bioinformatics.scm | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/gnu/packages/bioinformatics.scm
b/gnu/packages/bioinformatics.scm
index d82b6c0..c6acab1 100644
--- a/gnu/packages/bioinformatics.scm
+++ b/gnu/packages/bioinformatics.scm
@@ -7995,3 +7995,38 @@ immunoprecipitation and target enrichment on small
gene panels.  Thereby,
 CopywriteR constitutes a widely applicable alternative to available copy
 number detection tools.")
     (license license:gpl2)))
+
+(define-public r-sva
+  (package
+    (name "r-sva")
+    (version "3.22.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (bioconductor-uri "sva" version))
+       (sha265
+        (base32
+         "1wc1fjm6dzlsqqagm43y57w8jh8nsh0r0m8z1p6ximcb5gxqh7hn"))))
+    (build-system r-build-system)
+    (propagated-inputs
+     `(("r-genefilter" ,r-genefilter)))
+    (home-page "http://bioconductor.org/packages/sva")
+    (synopsis "Surrogate variable analysis")
+    (description
+     "This package contains functions for removing batch effects and
+other unwanted variation in high-throughput experiment. Specifically,
+the sva package contains functions for the identifying and building
+surrogate variables for high-dimensional data sets. Surrogate variables
+are covariates constructed directly from high-dimensional data (like gene
+expression/RNA sequencing/methylation/brain imaging data) that can be used
+in subsequent analyses to adjust for unknown, unmodeled, or latent sources
+of noise. The sva package can be used to remove artifacts in three ways:
+1. identifying and estimating surrogate variables for unknown sources of
+variation in high-throughput experiments Leek and Storey 2007 PLoS
Genetics,
+ 2008 PNAS,2. directly removing known batch effects using ComBat
+Johnson et al. 2007 Biostatistics and 3. removing batch effects with known
+control probes Leek 2014 biorXiv. Removing batch effects and using
surrogate
+variables in differential expression analysis have been shown to reduce
+dependence, stabilize error rate estimates, and improve reproducibility,
+see Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat.
Reviews Genetics.")
+    (license license:artistic2.0)))
--
1.9.1

[-- Attachment #1.2: Type: text/html, Size: 3059 bytes --]

[-- Attachment #2: 0001-gnu-Add-r-sva.patch --]
[-- Type: application/octet-stream, Size: 2532 bytes --]

From 821e0f26e2ee8cb6213fe5920fcd71a7f1c64985 Mon Sep 17 00:00:00 2001
From: Raoul Jean Pierre Bonnal <ilpuccio.febo@gmail.com>
Date: Fri, 13 Jan 2017 20:38:25 +0100
Subject: [PATCH] gnu: Add r-sva

* gnu/packages/bioinformatics.scm (r-sva): New variable.
---
 gnu/packages/bioinformatics.scm | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm
index d82b6c0..c6acab1 100644
--- a/gnu/packages/bioinformatics.scm
+++ b/gnu/packages/bioinformatics.scm
@@ -7995,3 +7995,38 @@ immunoprecipitation and target enrichment on small gene panels.  Thereby,
 CopywriteR constitutes a widely applicable alternative to available copy
 number detection tools.")
     (license license:gpl2)))
+
+(define-public r-sva
+  (package
+    (name "r-sva")
+    (version "3.22.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (bioconductor-uri "sva" version))
+       (sha265
+        (base32
+         "1wc1fjm6dzlsqqagm43y57w8jh8nsh0r0m8z1p6ximcb5gxqh7hn"))))
+    (build-system r-build-system)
+    (propagated-inputs
+     `(("r-genefilter" ,r-genefilter)))
+    (home-page "http://bioconductor.org/packages/sva")
+    (synopsis "Surrogate variable analysis")
+    (description
+     "This package contains functions for removing batch effects and
+other unwanted variation in high-throughput experiment. Specifically,
+the sva package contains functions for the identifying and building
+surrogate variables for high-dimensional data sets. Surrogate variables
+are covariates constructed directly from high-dimensional data (like gene
+expression/RNA sequencing/methylation/brain imaging data) that can be used
+in subsequent analyses to adjust for unknown, unmodeled, or latent sources
+of noise. The sva package can be used to remove artifacts in three ways:
+1. identifying and estimating surrogate variables for unknown sources of
+variation in high-throughput experiments Leek and Storey 2007 PLoS Genetics,
+ 2008 PNAS,2. directly removing known batch effects using ComBat
+Johnson et al. 2007 Biostatistics and 3. removing batch effects with known
+control probes Leek 2014 biorXiv. Removing batch effects and using surrogate
+variables in differential expression analysis have been shown to reduce
+dependence, stabilize error rate estimates, and improve reproducibility,
+see Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics.")
+    (license license:artistic2.0)))
-- 
1.9.1