Partial least squares with structured output for modelling the metabolomics data obtained from complex experimental designs: A study into the ϒ-block coding

Abstract

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a “pure” regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix ϒ that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of ϒ coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new ϒ coding can improve the interpretability of the PLS model compared to classic regression/classification coding.