Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation

作者：Sabina Bijlsma, I. Bobeldijk, Elwin Verheij, Raymond Ramaker, Sunil Kochhar, Ian Macdonald, Ben van Ommen, Age K. Smilde · 发表于：Analytical Chemistry · 年份：2005 · DOI：10.1021/ac051495j · 被引用次数：884 · 研究领域：Metabolomics and Mass Spectrometry Studies、Spectroscopy and Chemometric Analyses、Advanced Chemical Sensor Technologies

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.