gnu: Add r-ropls.

* gnu/packages/bioconductor.scm (r-ropls): New variable.
master
Ricardo Wurmus 2019-06-10 10:58:30 +02:00
parent 4252dace19
commit a9fac3f4d3
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@ -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)))