Imperial College London
Alternate

Dr Richard H. Barton

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 3014r.barton Website

 
 
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Location

 

SAFB 660Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Fonville:2010:10.1002/cem.1359,
author = {Fonville, JM and Richards, SE and Barton, RH and Boulange, CL and Ebbels, TMD and Nicholson, JK and Holmes, EC and Dumas, ME},
doi = {10.1002/cem.1359},
journal = {J. Chemometrics},
pages = {636--649},
title = {The Evolution of Partial Least Squares Models and Related Chemometric Approaches in Metabonomics and Metabolic Phenotyping},
url = {http://dx.doi.org/10.1002/cem.1359},
volume = {24},
year = {2010}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts ofquantitative or qualitative metabolic data. Understanding of the global function of the living organism can beachieved by integration of ‘omics’ approaches including metabonomics, genomics, transcriptomics and proteomics,increasing the complexity of the full data sets. Multivariate statistical approaches are well suited to extract thecharacterizing metabolic information associated with each level of dynamic process. In this review, we discusstechniques that have evolved from principal component analysis and partial least squares (PLS) methods with a focuson improved interpretation and modeling with respect to biomarker recovery and data visualization in the context ofmetabonomic applications. Visualization is of paramount importance to investigate complex metabolic signatures,the power and potential of which is illustrated with key papers. Recent improvements based on the removal oforthogonal variation are discussed in terms of interpretation enhancement, and are supported by relevantapplications. Flexibility of PLS methods in general and of O-PLS in particular allows implementation of derivativemethods such as O2-PLS, O-PLS-variance components, nonlinear methods, and batch modeling to improve analysis ofcomplex data sets, which facilitates extraction of information related to subtle biological processes. These approachescan be used to address issues present in complex multi-factorial data sets. Thus, we highlight the key advantages andlimitations of the different latent variable applications for top-down systems biology and assess the differencesbetween the methods available.
AU  - Fonville,JM
AU  - Richards,SE
AU  - Barton,RH
AU  - Boulange,CL
AU  - Ebbels,TMD
AU  - Nicholson,JK
AU  - Holmes,EC
AU  - Dumas,ME
DO  - 10.1002/cem.1359
EP  - 649
PY  - 2010///
SP  - 636
TI  - The Evolution of Partial Least Squares Models and Related Chemometric Approaches in Metabonomics and Metabolic Phenotyping
T2  - J. Chemometrics
UR  - http://dx.doi.org/10.1002/cem.1359
UR  - http://wileyonlinelibrary.com/journal/cem
VL  - 24
ER  -
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