A bayesian mixture model for comparative spectral count data in shotgun proteomics

James G. Booth, Kirsten E. Eilertson, Paul Dominic B. Olinares, Haiyuan Yu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.

Original languageEnglish (US)
JournalMolecular and Cellular Proteomics
Issue number8
StatePublished - Aug 1 2011

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry
  • Molecular Biology


Dive into the research topics of 'A bayesian mixture model for comparative spectral count data in shotgun proteomics'. Together they form a unique fingerprint.

Cite this