Package: ropls
Type: Package
Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and
        feature selection of omics data
Version: 1.45.0
Date: 2024-09-13
Authors@R: 
    person("Etienne A.", "Thevenot", email = "etienne.thevenot@cea.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1019-4577"))
biocViews: Regression, Classification, PrincipalComponent,
        Transcriptomics, Proteomics, Metabolomics, Lipidomics,
        MassSpectrometry, ImmunoOncology
Description: Latent variable modeling with Principal Component Analysis
        (PCA) and 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. 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. Successful applications of these chemometrics
        techniques include spectroscopic data such as Raman
        spectroscopy, nuclear magnetic resonance (NMR), mass
        spectrometry (MS) in metabolomics and proteomics, but also
        transcriptomics 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). The package can be accessed via a
        user interface on the Workflow4Metabolomics.org online resource
        for computational metabolomics (built upon the Galaxy
        environment).
Depends: R (>= 3.5.0)
Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats,
        MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils
Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, phenomis,
        rmarkdown, testthat
VignetteBuilder: knitr
License: CeCILL
Encoding: UTF-8
LazyLoad: yes
URL: https://doi.org/10.1021/acs.jproteome.5b00354
NeedsCompilation: no
RoxygenNote: 7.2.3
git_url: https://git.bioconductor.org/packages/ropls
git_branch: master
git_last_commit: 4d77cd4
git_last_commit_date: 2022-04-26
Packaged: 2026-05-30 09:41:08 UTC; root
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libssl-dev
        zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:41:32 UTC
RemoteUrl: https://github.com/bioc/ropls
RemoteRef: HEAD
RemoteSha: a70ae7c2e27a7e029ce8a2520a133b082b90186c
Author: Etienne A. Thevenot [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-1019-4577>)
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
Built: R 4.6.0; ; 2026-05-30 10:04:57 UTC; windows
