diff options
Diffstat (limited to 'gnu/packages/bioconductor.scm')
| -rw-r--r-- | gnu/packages/bioconductor.scm | 79 |
1 files changed, 75 insertions, 4 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index 5ffb7c4e3c..ff159638b3 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -3621,14 +3621,14 @@ investigation using RNA-seq data.") (define-public r-aucell (package (name "r-aucell") - (version "1.6.0") + (version "1.6.1") (source (origin (method url-fetch) (uri (bioconductor-uri "AUCell" version)) (sha256 (base32 - "025q1as9pifbxa7hidlz634q6d7l73zx8mqy4rjbfrk7d5615xvm")))) + "1vd8w6dygn1b5bwlha09mm6fbwyj07pmawpv53agcg1y7jlxs31b")))) (properties `((upstream-name . "AUCell"))) (build-system r-build-system) (propagated-inputs @@ -4438,14 +4438,14 @@ interpretation.") (define-public r-rhisat2 (package (name "r-rhisat2") - (version "1.0.1") + (version "1.0.2") (source (origin (method url-fetch) (uri (bioconductor-uri "Rhisat2" version)) (sha256 (base32 - "01jhj5vvfl4n2d0nl3nd1iw9nii85mgw2adnrmxb8wwlxgy240vr")))) + "1y3zqvk1vbcb10r1myh6f5yzjvf7bhwhpiq78bs1k6spli4bzj0q")))) (properties `((upstream-name . "Rhisat2"))) (build-system r-build-system) (native-inputs @@ -4606,3 +4606,74 @@ 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))) + +(define-public r-biosigner + (package + (name "r-biosigner") + (version "1.12.0") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "biosigner" version)) + (sha256 + (base32 + "1643iya40v6whb7lw7y34w5sanbasvj4yhvcygbip667yhphyv5b")))) + (build-system r-build-system) + (propagated-inputs + `(("r-biobase" ,r-biobase) + ("r-e1071" ,r-e1071) + ("r-randomforest" ,r-randomforest) + ("r-ropls" ,r-ropls))) + (native-inputs + `(("r-knitr" ,r-knitr) + ("r-rmarkdown" ,r-rmarkdown) + ("pandoc" ,ghc-pandoc) + ("pandoc-citeproc" ,ghc-pandoc-citeproc))) ; all for vignettes + (home-page "https://bioconductor.org/packages/biosigner/") + (synopsis "Signature discovery from omics data") + (description + "Feature selection is critical in omics data analysis to extract +restricted and meaningful molecular signatures from complex and high-dimension +data, and to build robust classifiers. This package implements a method to +assess the relevance of the variables for the prediction performances of the +classifier. The approach can be run in parallel with the PLS-DA, Random +Forest, and SVM binary classifiers. The signatures and the corresponding +'restricted' models are returned, enabling future predictions on new +datasets.") + (license license:cecill))) |