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Metabolites 2016, 6(4), 38; doi:10.3390/metabo6040038

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding

1
School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
2
School of Chemistry, Umm Al-Qura University, Al Taif Road, Mecca 24382, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Peter D. Karp
Received: 31 August 2016 / Revised: 20 October 2016 / Accepted: 24 October 2016 / Published: 28 October 2016
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
View Full-Text   |   Download PDF [1163 KB, uploaded 28 October 2016]   |  

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 Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y 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 Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding. View Full-Text
Keywords: partial least squares; structural modelling; experimental design; metabolomics; Y coding partial least squares; structural modelling; experimental design; metabolomics; Y coding
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Xu, Y.; Muhamadali, H.; Sayqal, A.; Dixon, N.; Goodacre, R. Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding. Metabolites 2016, 6, 38.

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