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Article

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
Metabolites 2016, 6(4), 38; https://doi.org/10.3390/metabo6040038
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)
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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MDPI and ACS Style

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. https://doi.org/10.3390/metabo6040038

AMA Style

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(4):38. https://doi.org/10.3390/metabo6040038

Chicago/Turabian Style

Xu, Yun, Howbeer Muhamadali, Ali Sayqal, Neil Dixon, and Royston Goodacre. 2016. "Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding" Metabolites 6, no. 4: 38. https://doi.org/10.3390/metabo6040038

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