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Comparison of Bi- and Tri-Linear PLS Models for Variable Selection in Metabolomic Time-Series Experiments

Department of Nutrition, Exercise and Sports, University of Copenhagen, 1958 Frederiksberg, Denmark
Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
Author to whom correspondence should be addressed.
Metabolites 2019, 9(5), 92;
Received: 3 March 2019 / Revised: 7 May 2019 / Accepted: 8 May 2019 / Published: 9 May 2019
PDF [5687 KB, uploaded 9 May 2019]


Metabolomic studies with a time-series design are widely used for discovery and validation of biomarkers. In such studies, changes of metabolic profiles over time under different conditions (e.g., control and intervention) are compared, and metabolites responding differently between the conditions are identified as putative biomarkers. To incorporate time-series information into the variable (biomarker) selection in partial least squares regression (PLS) models, we created PLS models with different combinations of bilinear/trilinear X and group/time response dummy Y. In total, five PLS models were evaluated on two real datasets, and also on simulated datasets with varying characteristics (number of subjects, number of variables, inter-individual variability, intra-individual variability and number of time points). Variables showing specific temporal patterns observed visually and determined statistically were labelled as discriminating variables. Bootstrapped-VIP scores were calculated for variable selection and the variable selection performance of five PLS models were assessed based on their capacity to correctly select the discriminating variables. The results showed that the bilinear PLS model with group × time response as dummy Y provided the highest recall (true positive rate) of 83–95% with high precision, independent of most characteristics of the datasets. Trilinear PLS models tend to select a small number of variables with high precision but relatively high false negative rate (lower power). They are also less affected by the noise compared to bilinear PLS models. In datasets with high inter-individual variability, bilinear PLS models tend to provide higher recall while trilinear models tend to provide higher precision. Overall, we recommend bilinear PLS with group x time response Y for variable selection applications in metabolomics intervention time series studies. View Full-Text
Keywords: time series; PLS; NPLS; variable selection; bootstrapped-VIP time series; PLS; NPLS; variable selection; bootstrapped-VIP

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Gao, Q.; Dragsted, L.O.; Ebbels, T. Comparison of Bi- and Tri-Linear PLS Models for Variable Selection in Metabolomic Time-Series Experiments. Metabolites 2019, 9, 92.

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