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author | Ricardo Wurmus <rekado@elephly.net> | 2019-06-10 10:58:39 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2019-06-10 13:41:48 +0200 |
commit | 075a90946b93eefeb30996978dd293147aaeff94 (patch) | |
tree | c3b9afef2b0583130369fd1890c1e9ad633cb42f /gnu/packages | |
parent | a9fac3f4d3e8fb579314afc6d22add1394f2fe7f (diff) |
gnu: Add r-biosigner.
* gnu/packages/bioconductor.scm (r-biosigner): New variable.
Diffstat (limited to 'gnu/packages')
-rw-r--r-- | gnu/packages/bioconductor.scm | 35 |
1 files changed, 35 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index f8bcb8eb4c..ff159638b3 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -4642,3 +4642,38 @@ validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients).") (license license:cecill))) + +(define-public r-biosigner + (package + (name "r-biosigner") + (version "1.12.0") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "biosigner" version)) + (sha256 + (base32 + "1643iya40v6whb7lw7y34w5sanbasvj4yhvcygbip667yhphyv5b")))) + (build-system r-build-system) + (propagated-inputs + `(("r-biobase" ,r-biobase) + ("r-e1071" ,r-e1071) + ("r-randomforest" ,r-randomforest) + ("r-ropls" ,r-ropls))) + (native-inputs + `(("r-knitr" ,r-knitr) + ("r-rmarkdown" ,r-rmarkdown) + ("pandoc" ,ghc-pandoc) + ("pandoc-citeproc" ,ghc-pandoc-citeproc))) ; all for vignettes + (home-page "https://bioconductor.org/packages/biosigner/") + (synopsis "Signature discovery from omics data") + (description + "Feature selection is critical in omics data analysis to extract +restricted and meaningful molecular signatures from complex and high-dimension +data, and to build robust classifiers. This package implements a method to +assess the relevance of the variables for the prediction performances of the +classifier. The approach can be run in parallel with the PLS-DA, Random +Forest, and SVM binary classifiers. The signatures and the corresponding +'restricted' models are returned, enabling future predictions on new +datasets.") + (license license:cecill))) |