Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Journal of Proteome Research
Thao VuRobert Powers

Abstract

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various leve...Continue Reading

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Citations

Sep 24, 2020·International Journal of Molecular Sciences·Aneta BalcerczykGilles J P Rautureau
Nov 13, 2020·Analytical Chemistry·Arthur S EdisonSicong Zhang

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