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Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a<jats:italic>subjects<\/jats:italic>by<jats:italic>metabolites<\/jats:italic>by<jats:italic>time points<\/jats:italic>array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We introduce an unsupervised multiway analysis approach based on the CANDECOMP\/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of<jats:italic>fasting-state<\/jats:italic>data using principal component analysis,<jats:italic>T0-corrected<\/jats:italic>data (i.e., data corrected by subtracting fasting-state data) using a CP model and<jats:italic>full-dynamic<\/jats:italic>(i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our experiments show that it is crucial to analyze both<jats:italic>fasting-state<\/jats:italic>and<jats:italic>T0-corrected<\/jats:italic>data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of<jats:italic>T0-corrected<\/jats:italic>or<jats:italic>full-dynamic<\/jats:italic>data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-024-05686-w","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T14:02:38Z","timestamp":1709560958000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Analyzing postprandial metabolomics data using multiway models: a simulation study"],"prefix":"10.1186","volume":"25","author":[{"given":"Lu","family":"Li","sequence":"first","affiliation":[]},{"given":"Shi","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Barbara M.","family":"Bakker","sequence":"additional","affiliation":[]},{"given":"Huub","family":"Hoefsloot","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Chawes","sequence":"additional","affiliation":[]},{"given":"David","family":"Horner","sequence":"additional","affiliation":[]},{"given":"Morten A.","family":"Rasmussen","sequence":"additional","affiliation":[]},{"given":"Age K.","family":"Smilde","sequence":"additional","affiliation":[]},{"given":"Evrim","family":"Acar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"issue":"2","key":"5686_CR1","doi-asserted-by":"publisher","first-page":"375","DOI":"10.2337\/dc11-1593","volume":"35","author":"AL Harte","year":"2012","unstructured":"Harte AL, Varma MC, Tripathi G, McGee KC, Al-Daghri NM, Al-Attas OS, Sabico S, O\u2019Hare JP, Ceriello A, Saravanan P, et al. 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