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authorRicardo Wurmus <rekado@elephly.net>2017-01-16 12:19:09 +0100
committerRicardo Wurmus <rekado@elephly.net>2017-01-17 21:46:50 +0100
commit7c08afaf7bd86c543d46a1d3157a8a57bfed8af4 (patch)
treeecc4f1cef117957c07a031f5513e15e48a5cd73f /gnu
parent05c7e5fb76cf8ebc271595cf3e363e3b8678d7e0 (diff)
gnu: Add r-vsn.
* gnu/packages/bioinformatics.scm (r-vsn): New variable.
Diffstat (limited to 'gnu')
-rw-r--r--gnu/packages/bioinformatics.scm33
1 files changed, 33 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm
index 9e073843e2..82a60dae05 100644
--- a/gnu/packages/bioinformatics.scm
+++ b/gnu/packages/bioinformatics.scm
@@ -8330,3 +8330,36 @@ CDF file formats.")
"This package contains functions for exploratory oligonucleotide array
analysis.")
(license license:lgpl2.0+)))
+
+(define-public r-vsn
+ (package
+ (name "r-vsn")
+ (version "3.42.3")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (bioconductor-uri "vsn" version))
+ (sha256
+ (base32
+ "0mgl0azys2g90simf8wx6jdwd7gyg3m4pf12n6w6507jixm2cg97"))))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-affy" ,r-affy)
+ ("r-biobase" ,r-biobase)
+ ("r-ggplot2" ,r-ggplot2)
+ ("r-limma" ,r-limma)))
+ (home-page "http://bioconductor.org/packages/release/bioc/html/vsn.html")
+ (synopsis "Variance stabilization and calibration for microarray data")
+ (description
+ "The package implements a method for normalising microarray intensities,
+and works for single- and multiple-color arrays. It can also be used for data
+from other technologies, as long as they have similar format. The method uses
+a robust variant of the maximum-likelihood estimator for an
+additive-multiplicative error model and affine calibration. The model
+incorporates data calibration step (a.k.a. normalization), a model for the
+dependence of the variance on the mean intensity and a variance stabilizing
+data transformation. Differences between transformed intensities are
+analogous to \"normalized log-ratios\". However, in contrast to the latter,
+their variance is independent of the mean, and they are usually more sensitive
+and specific in detecting differential transcription.")
+ (license license:artistic2.0)))