Stronger findings from mass spectral data through multi-peak modeling

Suvitaival, T., Rogers, S. and Kaski, S. (2014) Stronger findings from mass spectral data through multi-peak modeling. BMC Bioinformatics, 15, p. 208. (doi:10.1186/1471-2105-15-208) (PMID:24947013) (PMCID:PMC4080774)

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Background: Mass spectrometry-based metabolomic analysis depends upon the identification of spectral peaks by their mass and retention time. Statistical analysis that follows the identification currently relies on one main peak of each compound. However, a compound present in the sample typically produces several spectral peaks due to its isotopic properties and the ionization process of the mass spectrometer device. In this work, we investigate the extent to which these additional peaks can be used to increase the statistical strength of differential analysis.

Results: We present a Bayesian approach for integrating data of multiple detected peaks that come from one compound. We demonstrate the approach through a simulated experiment and validate it on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) experiments for metabolomics and lipidomics. Peaks that are likely to be associated with one compound can be clustered by the similarity of their chromatographic shape. Changes of concentration between sample groups can be inferred more accurately when multiple peaks are available.

Conclusion: When the sample-size is limited, the proposed multi-peak approach improves the accuracy at inferring covariate effects. An R implementation and data are available at

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:BMC Bioinformatics
Publisher:Biomed Central
ISSN (Online):1471-2105
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in BMC Bioinformatics 15:208
Publisher Policy:Reproduced under a Creative Commons License

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