Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations

Suvitaival, T., Rogers, S. and Kaski, S. (2014) Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations. Bioinformatics, 30(17), i461-i467. (doi:10.1093/bioinformatics/btu455) (PMID:25161234) (PMCID:PMC4147908)

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Motivation: Data analysis for metabolomics suffers from uncertainty because of the noisy measurement technology and the small sample size of experiments. Noise and the small sample size lead to a high probability of false findings. Further, individual compounds have natural variation between samples, which in many cases renders them unreliable as biomarkers. However, the levels of similar compounds are typically highly correlated, which is a phenomenon that we model in this work.

Results: We propose a hierarchical Bayesian model for inferring differences between groups of samples more accurately in metabolomic studies, where the observed compounds are collinear. We discover that the method decreases the error of weak and non-existent covariate effects, and thereby reduces false-positive findings. To achieve this, the method makes use of the mass spectral peak data by clustering similar peaks into latent compounds, and by further clustering latent compounds into groups that respond in a coherent way to the experimental covariates. We demonstrate the method with three simulated studies and validate it with a metabolomic benchmark dataset.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon
Authors: Suvitaival, T., Rogers, S., and Kaski, S.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN (Online):1460-2059
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Bioinformatics 30(17):i461-i467
Publisher Policy:Reproduced under a Creative Commons License

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