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

Sabina Bijlsma; Ivana Bobeldijk; Elwin R Verheij; Raymond Ramaker; Sunil Kochhar; Ian A Macdonald; Ben van Ommen; Age K Smilde

doi:10.1021/ac051495j

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

Sabina Bijlsma·Ivana Bobeldijk·Elwin R Verheij·Raymond Ramaker·Sunil Kochhar·Ian A Macdonald·Ben van Ommen·Age K Smilde

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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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Chromatography, Liquid Data Interpretation, Statistical Dietary Fats Europe Humans Least-Squares Analysis Lipids Mass Spectrometry Obesity Postprandial Period Journal Article Multicenter Study

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Sabina Bijlsma, Ivana Bobeldijk, Elwin R Verheij, Raymond Ramaker, Sunil Kochhar, Ian A Macdonald, Ben van Ommen, Age K Smilde. 2006-01-15. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.. https://doi.org/10.1021/ac051495j

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