  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.
  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.
  When the sample-size is limited, the proposed multi-peak approach improves
the accuracy at inferring covariate effects. An R implementation, data and the
supplementary material are available at
http://research.ics.aalto.fi/mi/software/peakANOVA/ .
