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| author | Ricardo Wurmus <rekado@elephly.net> | 2021-08-27 13:24:19 +0200 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2021-08-27 13:37:29 +0200 |
| commit | 17c765e2e1b425c5be06bb1701749656e3d84f4b (patch) | |
| tree | 47ccca806aea8659a4b1c8d86f6e84c1bf1fd599 | |
| parent | 63a5090869fac4f2b1c310d02b41d29669f3f389 (diff) | |
| download | guix-17c765e2e1b425c5be06bb1701749656e3d84f4b.tar.gz | |
gnu: Add r-cytonorm.
* gnu/packages/bioinformatics.scm (r-cytonorm): New variable.
| -rw-r--r-- | gnu/packages/bioinformatics.scm | 39 |
1 files changed, 39 insertions, 0 deletions
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm index e663ff2591..1775f5a15e 100644 --- a/gnu/packages/bioinformatics.scm +++ b/gnu/packages/bioinformatics.scm @@ -14903,3 +14903,42 @@ copy number estimation, as described by integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.") (license license:expat)))) + +(define-public r-cytonorm + (let ((commit "e4b9d343ee65db3c422800f1db3e77c25abde987") + (revision "1")) + (package + (name "r-cytonorm") + (version (git-version "0.0.7" revision commit)) + (source + (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/saeyslab/CytoNorm") + (commit commit))) + (file-name (git-file-name name version)) + (sha256 + (base32 + "0h2rdy15i4zymd4dv60n5w0frbsdbmzpv99dgm0l2dn041qv7fah")))) + (properties `((upstream-name . "CytoNorm"))) + (build-system r-build-system) + (propagated-inputs + `(("r-cytoml" ,r-cytoml) + ("r-dplyr" ,r-dplyr) + ("r-emdist" ,r-emdist) + ("r-flowcore" ,r-flowcore) + ("r-flowsom" ,r-flowsom) + ("r-flowworkspace" ,r-flowworkspace) + ("r-ggplot2" ,r-ggplot2) + ("r-gridextra" ,r-gridextra) + ("r-pheatmap" ,r-pheatmap) + ("r-stringr" ,r-stringr))) + (home-page "https://github.com/saeyslab/CytoNorm") + (synopsis "Normalize cytometry data measured across multiple batches") + (description + "This package can be used to normalize cytometry samples when a control +sample is taken along in each of the batches. This is done by first +identifying multiple clusters/cell types, learning the batch effects from the +control samples and applying quantile normalization on all markers of +interest.") + (license license:gpl2+)))) |