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author | Ricardo Wurmus <rekado@elephly.net> | 2019-03-27 15:45:20 +0100 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2019-03-27 16:17:20 +0100 |
commit | 305050b56d5df003f986cbca5fedb4b9b5cd45bb (patch) | |
tree | 740236be0ad3203ae8a9e8f5d4ae1bf5035a0d40 /gnu | |
parent | 11f226e124e51de8a7ca873c699badbb74be057c (diff) | |
download | guix-305050b56d5df003f986cbca5fedb4b9b5cd45bb.tar.gz guix-305050b56d5df003f986cbca5fedb4b9b5cd45bb.zip |
gnu: Add r-dose.
* gnu/packages/bioconductor.scm (r-dose): New variable.
Diffstat (limited to 'gnu')
-rw-r--r-- | gnu/packages/bioconductor.scm | 34 |
1 files changed, 34 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index 86c15c08ea..0c7be1c7d5 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -2779,3 +2779,37 @@ analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.") (license license:expat))) + +(define-public r-dose + (package + (name "r-dose") + (version "3.8.2") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "DOSE" version)) + (sha256 + (base32 + "1gh7dhvfc71kawxcfx8xqlir7mwvg5mmz4lqrdrvw5knvi2h3mfa")))) + (properties `((upstream-name . "DOSE"))) + (build-system r-build-system) + (propagated-inputs + `(("r-annotationdbi" ,r-annotationdbi) + ("r-biocparallel" ,r-biocparallel) + ("r-do-db" ,r-do-db) + ("r-fgsea" ,r-fgsea) + ("r-ggplot2" ,r-ggplot2) + ("r-gosemsim" ,r-gosemsim) + ("r-qvalue" ,r-qvalue) + ("r-reshape2" ,r-reshape2) + ("r-s4vectors" ,r-s4vectors))) + (home-page "https://guangchuangyu.github.io/software/DOSE/") + (synopsis "Disease ontology semantic and enrichment analysis") + (description + "This package implements five methods proposed by Resnik, Schlicker, +Jiang, Lin and Wang, respectively, for measuring semantic similarities among +@dfn{Disease ontology} (DO) terms and gene products. Enrichment analyses +including hypergeometric model and gene set enrichment analysis are also +implemented for discovering disease associations of high-throughput biological +data.") + (license license:artistic2.0))) |