gnu: Add r-ropls.
* gnu/packages/bioconductor.scm (r-ropls): New variable.
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@ -4606,3 +4606,39 @@ expression data to predict switches in regulatory activity between two
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conditions. A Bayesian network is used to model the regulatory structure and
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Markov-Chain-Monte-Carlo is applied to sample the activity states.")
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(license license:gpl2+)))
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(define-public r-ropls
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(package
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(name "r-ropls")
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(version "1.16.0")
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(source
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(origin
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(method url-fetch)
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(uri (bioconductor-uri "ropls" version))
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(sha256
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(base32
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"099nv9dgmw3avkxv7cd27r16yj56svjlp5q4i389yp1n0r5zhyl2"))))
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(build-system r-build-system)
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(propagated-inputs `(("r-biobase" ,r-biobase)))
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(native-inputs
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`(("r-knitr" ,r-knitr))) ; for vignettes
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(home-page "https://dx.doi.org/10.1021/acs.jproteome.5b00354")
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(synopsis "Multivariate analysis and feature selection of omics data")
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(description
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"Latent variable modeling with @dfn{Principal Component Analysis} (PCA)
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and @dfn{Partial Least Squares} (PLS) are powerful methods for visualization,
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regression, classification, and feature selection of omics data where the
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number of variables exceeds the number of samples and with multicollinearity
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among variables. @dfn{Orthogonal Partial Least Squares} (OPLS) enables to
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separately model the variation correlated (predictive) to the factor of
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interest and the uncorrelated (orthogonal) variation. While performing
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similarly to PLS, OPLS facilitates interpretation.
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This package provides imlementations of PCA, PLS, and OPLS for multivariate
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analysis and feature selection of omics data. In addition to scores, loadings
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and weights plots, the package provides metrics and graphics to determine the
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optimal number of components (e.g. with the R2 and Q2 coefficients), check the
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validity of the model by permutation testing, detect outliers, and perform
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feature selection (e.g. with Variable Importance in Projection or regression
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coefficients).")
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(license license:cecill)))
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