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bioRxiv · 10.1101/2024.08.13.607724

Longitudinal metabolomics data analysis informed bymechanistic models

Li, L.·Hoefsloot, H. C.·Bakker, B. M.·Horner, D.·Rasmussen, M. A.·Smilde, A.·Acar, E.
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Abstract

MotivationMetabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements. ResultsWe arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets are coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. AvailabilityThe code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).

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Li, L., Hoefsloot, H. C., Bakker, B. M., Horner, D., Rasmussen, M. A., Smilde, A., Acar, E.. 2024-08-16. Longitudinal metabolomics data analysis informed bymechanistic models. https://doi.org/10.1101/2024.08.13.607724

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