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author | Ricardo Wurmus <rekado@elephly.net> | 2019-06-10 10:58:30 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2019-06-10 13:41:45 +0200 |
commit | a9fac3f4d3e8fb579314afc6d22add1394f2fe7f (patch) | |
tree | ceafd0d502f492388fcf679de1c853a2437c70ee /gnu/packages/bioconductor.scm | |
parent | 4252dace19945f56192477e8cb07973c20a526ba (diff) | |
download | patches-a9fac3f4d3e8fb579314afc6d22add1394f2fe7f.tar patches-a9fac3f4d3e8fb579314afc6d22add1394f2fe7f.tar.gz |
gnu: Add r-ropls.
* gnu/packages/bioconductor.scm (r-ropls): New variable.
Diffstat (limited to 'gnu/packages/bioconductor.scm')
-rw-r--r-- | gnu/packages/bioconductor.scm | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index c1bbcdc2c0..f8bcb8eb4c 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -4606,3 +4606,39 @@ expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states.") (license license:gpl2+))) + +(define-public r-ropls + (package + (name "r-ropls") + (version "1.16.0") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "ropls" version)) + (sha256 + (base32 + "099nv9dgmw3avkxv7cd27r16yj56svjlp5q4i389yp1n0r5zhyl2")))) + (build-system r-build-system) + (propagated-inputs `(("r-biobase" ,r-biobase))) + (native-inputs + `(("r-knitr" ,r-knitr))) ; for vignettes + (home-page "https://dx.doi.org/10.1021/acs.jproteome.5b00354") + (synopsis "Multivariate analysis and feature selection of omics data") + (description + "Latent variable modeling with @dfn{Principal Component Analysis} (PCA) +and @dfn{Partial Least Squares} (PLS) are powerful methods for visualization, +regression, classification, and feature selection of omics data where the +number of variables exceeds the number of samples and with multicollinearity +among variables. @dfn{Orthogonal Partial Least Squares} (OPLS) enables to +separately model the variation correlated (predictive) to the factor of +interest and the uncorrelated (orthogonal) variation. While performing +similarly to PLS, OPLS facilitates interpretation. + +This package provides imlementations of PCA, PLS, and OPLS for multivariate +analysis and feature selection of omics data. In addition to scores, loadings +and weights plots, the package provides metrics and graphics to determine the +optimal number of components (e.g. with the R2 and Q2 coefficients), check the +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))) |