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bioRxiv · 10.64898/2026.02.01.703114

Revealing Subject-Specific Temporal Patterns from Longitudinal Data

Chatzis, C.·Horner, D.·Bro, R.·Schoos, A.-M. M.·Rasmussen, M. A.·Acar, E.
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Abstract

MotivationTemporal multivariate data is ubiquitous in many domains, for instance, being collected over time at planned visits (every few months/years) in longitudinal cohorts, or every few minutes/hours in challenge tests. The analysis of such data often focuses on revealing the underlying temporal patterns common across subjects. However, there are subject-specific differences in temporal patterns, which hold the promise to enhance our understanding of underlying mechanisms and facilitate personalized approaches. Nevertheless, extracting subject-specific temporal patterns from longitudinal multivariate data reliably is an open challenge. ResultsWe introduce coupled matrix factorizations (CMF) as effective tools to capture subject-specific temporal patterns focusing on two novel applications: analysis of longitudinal metabolomics data and sensitization data. Our analysis shows that CMF models reliably capture subject-specific (shape) differences in temporal patterns revealing further in-sights compared to the state of the art. In metabolomics, CMF models reveal differences in metabolic responses of individuals (in a postprandial meal challenge) according to anthropometric and insulin sensitivity measures. In sensitization data analysis, CMF-based methods capture differences in temporal trajectories of children according to delivery/birth mode. We demonstrate the reliability of extracted patterns using reproducibility and replicability. AvailabilityThe code is available on https://github.com/cchatzis/Revealing-Subject-specific-Temporal-Patterns-from-Longitudinal-Data. Clinical data is not publicly available due to privacy reasons. Data can be made available under a joint research collaboration by contacting COPSAC (administration@dbac.dk).

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Chatzis, C., Horner, D., Bro, R., Schoos, A.-M. M., Rasmussen, M. A., Acar, E.. 2026-02-03. Revealing Subject-Specific Temporal Patterns from Longitudinal Data. https://doi.org/10.64898/2026.02.01.703114

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