Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

131Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Posma:2018:10.1021/acs.jproteome.7b00879,
author = {Posma, JM and Garcia, Perez I and Ebbels, TMD and Lindon, JC and Stamler, J and Elliott, P and Holmes, E and Nicholson, J},
doi = {10.1021/acs.jproteome.7b00879},
journal = {Journal of Proteome Research},
pages = {1586--1595},
title = {Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data},
url = {http://dx.doi.org/10.1021/acs.jproteome.7b00879},
volume = {17},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Posma,JM
AU - Garcia,Perez I
AU - Ebbels,TMD
AU - Lindon,JC
AU - Stamler,J
AU - Elliott,P
AU - Holmes,E
AU - Nicholson,J
DO - 10.1021/acs.jproteome.7b00879
EP - 1595
PY - 2018///
SN - 1535-3893
SP - 1586
TI - Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data
T2 - Journal of Proteome Research
UR - http://dx.doi.org/10.1021/acs.jproteome.7b00879
UR - http://hdl.handle.net/10044/1/57292
VL - 17
ER -