Comparison of Bi- and Tri-Linear PLS models for variable selection in metabolomic time-series experiments

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Title: Comparison of Bi- and Tri-Linear PLS models for variable selection in metabolomic time-series experiments
Authors: Gao, Q
Dragsted, LO
Ebbels, T
Item Type: Journal Article
Abstract: 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.
Issue Date: 8-May-2019
Date of Acceptance: 8-May-2019
URI: http://hdl.handle.net/10044/1/71961
DOI: https://doi.org/10.3390/metabo9050092
ISSN: 2218-1989
Publisher: MDPI
Journal / Book Title: Metabolites
Volume: 9
Issue: 5
Copyright Statement: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
time series
PLS
NPLS
variable selection
bootstrapped-VIP
BIOMARKER DISCOVERY
COMPONENT ANALYSIS
REGRESSION
TOOL
NPLS
PLS
bootstrapped-VIP
time series
variable selection
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
time series
PLS
NPLS
variable selection
bootstrapped-VIP
BIOMARKER DISCOVERY
COMPONENT ANALYSIS
REGRESSION
TOOL
Publication Status: Published
Open Access location: https://www.mdpi.com/2218-1989/9/5/92
Article Number: 92
Online Publication Date: 2019-05-09
Appears in Collections:Division of Surgery



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