Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pra...

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Bibliographic Details
Published in:Analytical chemistry (Washington) Vol. 78; no. 2; p. 567
Main Authors: Bijlsma, Sabina, Bobeldijk, Ivana, Verheij, Elwin R, Ramaker, Raymond, Kochhar, Sunil, Macdonald, Ian A, van Ommen, Ben, Smilde, Age K
Format: Journal Article
Language:English
Published: United States 15.01.2006
Subjects:
Chromatography, Liquid
Data Interpretation, Statistical
Dietary Fats - administration & dosage
Dietary Fats - blood
Europe
Humans
Least-Squares Analysis
Lipids - blood
Mass Spectrometry
Obesity - blood
Postprandial Period - physiology
Europe
ISSN:0003-2700
Online Access:Get more information
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Summary:A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.
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ISSN:0003-2700
DOI:10.1021/ac051495j