title: Stronger findings from mass spectral data through multi-peak modeling.
abstract: 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/.
authors.forename: Tommi
authors.initials: T
authors.lastname: Suvitaival
authors.forename: Simon
authors.initials: S
authors.lastname: Rogers
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, 00076 Espoo, Finland. samuel.kaski@aalto.fi.
authors.forename: Samuel
authors.initials: S
authors.lastname: Kaski
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D008055:Lipids / Q000032:analysis; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics
chemical_list: D008055:Lipids