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.

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).

","plain":"Latent variable modeling with \"Principal Component Analysis\" (PCA) and\n\"Partial Least Squares\" (PLS) are powerful methods for visualization,\nregression, classification, and feature selection of omics data where\nthe number of variables exceeds the number of samples and with\nmulticollinearity among variables. \"Orthogonal Partial Least Squares\"\n(OPLS) enables to separately model the variation correlated (predictive)\nto the factor of interest and the uncorrelated (orthogonal) variation.\nWhile performing similarly to PLS, OPLS facilitates interpretation.\n\nThis package provides imlementations of PCA, PLS, and OPLS for\nmultivariate analysis and feature selection of omics data. In addition\nto scores, loadings and weights plots, the package provides metrics and\ngraphics to determine the optimal number of components (e.g. with the\nR2 and Q2 coefficients), check the validity of the model by permutation\ntesting, detect outliers, and perform feature selection (e.g. with\nVariable Importance in Projection or regression coefficients).\n\n","locale":"en_US.UTF-8"},"home-page":"https://dx.doi.org/10.1021/acs.jproteome.5b00354","derivations":[{"system":"x86_64-linux","target":"","derivation":"/gnu/store/vwqbah5hisjcwdz3f863yvp42rqf6iqd-r-ropls-1.18.6.drv"},{"system":"mips64el-linux","target":"","derivation":"/gnu/store/m3rk6mjswq1f2azpzm7b3g80z3ng1wnd-r-ropls-1.18.6.drv"},{"system":"i686-linux","target":"","derivation":"/gnu/store/f0vdw2089fd610j2hq9g05n3nglzva9p-r-ropls-1.18.6.drv"},{"system":"armhf-linux","target":"","derivation":"/gnu/store/ghlb1d7y4lxfra80jazd4f7dgr0irxpz-r-ropls-1.18.6.drv"},{"system":"aarch64-linux","target":"","derivation":"/gnu/store/2ba81b3yp7z1hiravq83vbl2djc9b4m8-r-ropls-1.18.6.drv"}]}