[PATCH] gnu: Add r-sva

[Ra]

* 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

0001-gnu-Add-r-sva.patch
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