title: A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.
abstract: The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
authors.forename: Piotr S
authors.initials: PS
authors.lastname: Gromski
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
authors.forename: Howbeer
authors.initials: H
authors.lastname: Muhamadali
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
authors.forename: David I
authors.initials: DI
authors.lastname: Ellis
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
authors.forename: Yun
authors.initials: Y
authors.lastname: Xu
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
authors.forename: Elon
authors.initials: E
authors.lastname: Correa
authors.affiliation: School of Chemistry, Brunswick Street, The University of Manchester, Manchester M13 9PL, UK.
authors.forename: Michael L
authors.initials: ML
authors.lastname: Turner
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. Electronic address: roy.goodacre@manchester.ac.uk.
authors.forename: Royston
authors.initials: R
authors.lastname: Goodacre
mesh_terms: D000818:Animals; D016002:Discriminant Analysis; D006801:Humans; D016018:Least-Squares Analysis; D055432:Metabolomics / Q000379:methods*; D060388:Support Vector Machine
keywords: Chemometrics; Metabolomics; Partial least squares-discriminant analysis; Principal component-discriminant function analysis; Random forests; Support vector machines