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Stronger findings from mass spectral data through multi-peak modeling

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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.Conclusions: 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 http://research.ics.aalto.fi/mi/software/peakANOVA/.

Original languageEnglish
Article number208
JournalBMC Bioinformatics
Volume15
Issue number1
ISSN1471-2105
DOIs
Publication statusPublished - 19 Jun 2014

    Research areas

  • ANOVA-type modeling, Bayesian modeling, Clustering, Lipidomics, Mass spectrometry, Metabolomics, Nonparametric Bayes

ID: 61172905