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Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data

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Title: Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data
Authors: Posma, JM
Garcia Perez, I
Ebbels, TMD
Lindon, JC
Stamler, J
Elliott, P
Holmes, E
Nicholson, J
Item Type: Journal Article
Abstract: Metabolism is altered by genetics, diet, disease status, environment and many other factors. Modelling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-Adjusted Projections to Latent Structures (CA-PLS) is exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
Editors: Yates III, JR
Issue Date: 19-Feb-2018
Date of Acceptance: 18-Feb-2018
URI: http://hdl.handle.net/10044/1/57292
DOI: https://dx.doi.org/10.1021/acs.jproteome.7b00879
ISSN: 1535-3893
Publisher: American Chemical Society
Start Page: 1586
End Page: 1595
Journal / Book Title: Journal of Proteome Research
Volume: 17
Issue: 4
Copyright Statement: © 2018 American Chemical Society. ACS AuthorChoice - This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Sponsor/Funder: Medical Research Council (MRC)
National Institute for Health Research
Public Health England
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council
Funder's Grant Number: G0801056B
NF-SI-0611-10136
6337091
MC_PC_12025
MR/L01632X/1
MR/L01341X/1
MR/L01632X/1
MR/S004033/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biochemistry & Molecular Biology
biomarker discovery
chemometrics
confounder elimination
covariate adjustment
metabolic phenotyping
Monte Carlo cross-validation
multivariate data analysis
random matrix theory
reanalysis
sampling bias
SURROGATE VARIABLE ANALYSIS
GENE-EXPRESSION
MULTIVARIATE CALIBRATION
H-1-NMR SPECTROSCOPY
METABONOMIC APPROACH
DIETARY BIOMARKERS
WIDE ASSOCIATION
CROSS-VALIDATION
MICROARRAY DATA
BLOOD-PRESSURE
re-analysis
06 Biological Sciences
03 Chemical Sciences
Publication Status: Published
Appears in Collections:Division of Surgery
Faculty of Medicine
Epidemiology, Public Health and Primary Care



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