The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping

Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts of quantitative or qualitative metabolic data. Understanding of the global function of the living organism can be achieved by integration of ‘omics’ approaches including metabonomics, genomi...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemometrics 2010-11, Vol.24 (11-12), p.636-649
Main Authors: Fonville, Judith M., Richards, Selena E., Barton, Richard H., Boulange, Claire L., Ebbels, Timothy M. D., Nicholson, Jeremy K., Holmes, Elaine, Dumas, Marc-Emmanuel
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biology
Chemistry
Chemometrics
Data visualization
Derivatives
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General and physical chemistry
General. Nomenclature, chemical documentation, computer chemistry
Genomics
Genotype & phenotype
Least squares method
Mathematical models
Metabolism
metabonomics
multivariate
Multivariate analysis
O-PLS
partial least squares
Principal components analysis
Proteomics
Signatures
systems biology
Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts of quantitative or qualitative metabolic data. Understanding of the global function of the living organism can be achieved 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 the characterizing metabolic information associated with each level of dynamic process. In this review, we discuss techniques that have evolved from principal component analysis and partial least squares (PLS) methods with a focus on improved interpretation and modeling with respect to biomarker recovery and data visualization in the context of metabonomic 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 of orthogonal variation are discussed in terms of interpretation enhancement, and are supported by relevant applications. Flexibility of PLS methods in general and of O‐PLS in particular allows implementation of derivative methods such as O2‐PLS, O‐PLS‐variance components, nonlinear methods, and batch modeling to improve analysis of complex data sets, which facilitates extraction of information related to subtle biological processes. These approaches can be used to address issues present in complex multi‐factorial data sets. Thus, we highlight the key advantages and limitations of the different latent variable applications for top‐down systems biology and assess the differences between the methods available. Copyright © 2010 John Wiley & Sons, Ltd. Metabolic information from spectroscopic profiles can be extracted using multivariate statistical methods. In this review, various approaches to partial least squares modeling are discussed, with a particular focus on visualization, interpretation and biomarker recovery. The removal of orthogonal variation and other derivative methods are evaluated, and avenues for future research are highlighted. Methods are presented together with relevant applications to highlight the uses and limitations of available techniques.
ISSN:0886-9383
1099-128X
1099-128X
DOI:10.1002/cem.1359