Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation |
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Authors: | Bijlsma Sabina Bobeldijk Ivana Verheij Elwin R Ramaker Raymond Kochhar Sunil Macdonald Ian A van Ommen Ben Smilde Age K |
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Affiliation: | Business Unit Analytical Sciences and Business Unit Physiological Sciences, TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands. bijlsma@voeding.tno.nl |
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Abstract: | 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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