Print Email Facebook Twitter Exploring the analysis of structured metabolomics data Title Exploring the analysis of structured metabolomics data Author Verouden, M.P.H. Westerhuis, J.A. van der Werf, M.J. Smilde, A.K. TNO Kwaliteit van Leven Publication year 2009 Abstract In metabolomics research a large number of metabolites are measured that reflect the cellular state under the experimental conditions studied. In many occasions the experiments are performed according to an experimental design to make sure that sufficient variation is induced in the metabolite concentrations. However, as metabolomics is a holistic approach, also a large number of metabolites are measured in which no variation is induced by the experimental design. The presence of such non-induced metabolites hampers traditional data analysis methods as PCA to estimate the true model of the induced variation. The greediness of PCA leads to a clear overfit of the metabolomics data and can lead to a bad selection of important metabolites. In this paper we explore how, why and how severe PCA overfits data with an underlying experimental design. Recently new data analysis methods have been introduced that can use prior information of the system to reduce the overfit. We show that incorporation of prior knowledge of the system under investigation leads to a better estimation of the true underlying structure and to less overfit. The experimental design information together with ASCA is used to improve the analysis of metabolomics data. To show the improved model estimation property of ASCA a thorough simulation study is used and the results are extended to a microbial metabolomics batch fermentation study. The ASCA model is much less affected by the non-induced variation and measurement error than PCA, leading to a much better model of the induced variation. © 2009 Elsevier B.V. All rights reserved. Subject NutritionBiotechnologyASCAExperimental designMicrobial metabolomicsOverfitPCAanalysis of variance simultaneous component analysisarticlebatch fermentationcontrolled studymetabolomicsmicrobial metabolismprincipal component analysispriority journalstatistical analysisstatistical model To reference this document use: http://resolver.tudelft.nl/uuid:db4a2c69-0933-41b6-8bdc-adf7b0551eeb DOI https://doi.org/10.1016/j.chemolab.2009.05.004 TNO identifier 241699 ISSN 0169-7439 Source Chemometrics and Intelligent Laboratory Systems, 98 (1), 88-96 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.