libPLS: An Integrated Library for Partial Least Squares Regression and Discriminant Analysis
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Abstract
Partial least squares (PLS) have gained wide applications especially in chemometrics, metabolomics/metabonomics as well as bioinformatics. To our knowledge, an integrated PLS library that include not only basic PLS modeling algorithms but also advanced and/or recently developed methods on model assessment, outlier detection and variable selection is in lack. Here we present libPLS which provides an integrated platform for developing PLS regression and/or discriminant analysis (PLS-DA) models. This library is written in MATLAB and freely available at www.libpls.net.
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2014. libPLS: An Integrated Library for Partial Least Squares Regression and Discriminant Analysis. PeerJ PrePrints 2:e190v1 https://doi.org/10.7287/peerj.preprints.190v1Author comment
An easy-to-use library for building PLS or PLS-DA models for analyzing chemical or biological data.
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Competing Interests
There are no competing interests to declare.
Author Contributions
Hongdong Li conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper.
Qingsong Xu performed the experiments.
Yizeng Liang conceived and designed the experiments, contributed reagents/materials/analysis tools.
Funding
This work was financially supported by the National Nature Foundation Committee of P.R. China Grants No. 21075138. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
