Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models

Jeffrey S. Morris, Philip J. Brown, Richard C. Herrick, Keith A. Baggerly, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


In this article, we apply the recently developed Bayesian wavelet-based functional mixed model methodology to analyze MALDI-TOF mass spectrometry proteomic data. By modeling mass spectra as functions, this approach avoids reliance on peak detection methods. The flexibility of this framework in modeling nonparametric fixed and random effect functions enables it to model the effects of multiple factors simultaneously, allowing one to perform inference on multiple factors of interest using the same model fit, while adjusting for clinical or experimental covariates that may affect both the intensities and locations of peaks in the spectra. For example, this provides a straightforward way to account for systematic block and batch effects that characterize these data. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, in a way that takes both statistical and clinical significance into account and controls the Bayesian false discovery rate to a prespecified level. We apply this method to two cancer studies.

Original languageEnglish (US)
Pages (from-to)479-489
Number of pages11
Issue number2
StatePublished - Jun 2008
Externally publishedYes


  • Bayesian analysis
  • False discovery rate
  • Functional data analysis
  • Functional mixed models
  • Mass spectrometry
  • Proteomics

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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