title: Empirical Bayes analysis of quantitative proteomics experiments.
abstract: BACKGROUND
  Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.
  
  
  RESULTS
  We analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3' UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data.
  
  
  CONCLUSIONS
  Our results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.
authors.affiliation: Cancer program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. margolin@broad.mit.edu
authors.forename: Adam A
authors.initials: AA
authors.lastname: Margolin
authors.forename: Shao-En
authors.initials: SE
authors.lastname: Ong
authors.forename: Monica
authors.initials: M
authors.lastname: Schenone
authors.forename: Robert
authors.initials: R
authors.lastname: Gould
authors.forename: Stuart L
authors.initials: SL
authors.lastname: Schreiber
authors.forename: Steven A
authors.initials: SA
authors.lastname: Carr
authors.forename: Todd R
authors.initials: TR
authors.lastname: Golub
mesh_terms: D020413:3' Untranslated Regions; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D005189:False Positive Reactions; D006367:HeLa Cells; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D020413:3' Untranslated Regions; D020543:Proteome