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This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see ropls.

PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

Bioconductor version: 3.4

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 online resource for computational metabolomics (built upon the Galaxy environment).

Author: Etienne A. Thevenot <etienne.thevenot at>

Maintainer: Etienne A. Thevenot <etienne.thevenot at>

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biocViews Classification, Lipidomics, MassSpectrometry, Metabolomics, PrincipalComponent, Proteomics, Regression, Software, Transcriptomics
Version 1.6.2
In Bioconductor since BioC 3.2 (R-3.2) (1.5 years)
License CeCILL
Imports Biobase, methods
Suggests BiocGenerics, BiocStyle, CAMERA, faahKO, knitr, multtest, rmarkdown, RUnit, xcms
Depends On Me
Imports Me biosigner
Suggests Me proFIA
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