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

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Author: Bijlsma, S. · Bobeldijk, I. · Verheij, E.R. · Ramaker, R. · Kochhar, S. · Macdonald, I.A. · Ommen, B. van · Smilde, A.K.
Institution: TNO Kwaliteit van Leven
Source:Analytical Chemistry, 2, 78, 567-574
Identifier: 239091
doi: doi:10.1021/ac051495j
Keywords: Biology · Analytical research · Biomedical research · Data processing · Data reduction · Liquid chromatography · Mass spectrometry · Metabolism · Statistical methods · Biomarker models · Human metabolomics · Metabolic profiling · Partial least-squares discriminant analysis (PLS-DA) · Plasmas · article · discriminant analysis · fat intake · human · human tissue · information processing · lean body weight · liquid chromatography · mass spectrometry · metabolomics · obesity · plasma · regression analysis · statistical analysis · test meal · univariate analysis · validation study · Chromatography, Liquid · Data Interpretation, Statistical · Dietary Fats · Europe · Humans · Least-Squares Analysis · Lipids · Mass Spectrometry · Obesity · Postprandial Period


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. © 2006 American Chemical Society.