Stronger findings from mass spectral data through multi-peak modeling

BMC Bioinformatics

Tommi Suvitaival³  Simon Rogers²  Samuel Kaski¹ 

[1] Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland

[2] School of Computing Science, University of Glasgow, G12 8QQ, Glasgow, UK

[3] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Espoo, Finland

关键词: Nonparametric Bayes;    Lipidomics;    Metabolomics;    Mass spectrometry;    Clustering;    Bayesian modeling;    ANOVA-type modeling;   

Others  :  818395
DOI  :  10.1186/1471-2105-15-208

 received in 2014-03-19, accepted in 2014-06-12,  发布年份 2014

【 摘 要 】

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/ webcite.

【 授权许可】

   
2014 Suvitaival et al.; licensee BioMed Central Ltd.

【 图 表 】

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