Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation
article
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.
Topics
Analytical researchBiomedical researchData processingData reductionLiquid chromatographyMass spectrometryMetabolismStatistical methodsBiomarker modelsHuman metabolomicsMetabolic profilingPartial least-squares discriminant analysis (PLS-DA)PlasmasDiscriminant analysisFat intakeHumanHuman tissueInformation processingLean body weightLiquid chromatographyMass spectrometryMetabolomicsObesityPlasmaRegression analysisStatistical analysisTest mealUnivariate analysisValidation studyChromatography, LiquidData Interpretation, StatisticalDietary FatsEuropeHumansLeast-Squares AnalysisLipidsMass SpectrometryObesityPostprandial Period
TNO Identifier
239091
ISSN
0003-2700
Source
Analytical Chemistry, 78(2), pp. 567-574.
Pages
567-574
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