Empirical bayes analysis of quantitative proteomics experiments

Adam Margolin, Shao En Ong, Monica Schenone, Robert Gould, Stuart L. Schreiber, Steven A. Carr, Todd R. Golub

Research output: Contribution to journalArticle

38 Citations (Scopus)

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. Methodology/Principal Findings: 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/Significance: 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.

Original languageEnglish (US)
Article numbere7454
JournalPLoS One
Volume4
Issue number10
DOIs
StatePublished - Oct 14 2009
Externally publishedYes

Fingerprint

Proteomics
proteomics
quantitative analysis
Statistical Data Interpretation
statistical analysis
MicroRNAs
microRNA
Statistical methods
Proteins
proteins
Experiments
3' Untranslated Regions
3' untranslated regions
Proteome
proteome
Information Systems
Purification
analytical methods
Mass spectrometry
Sequence Analysis

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Margolin, A., Ong, S. E., Schenone, M., Gould, R., Schreiber, S. L., Carr, S. A., & Golub, T. R. (2009). Empirical bayes analysis of quantitative proteomics experiments. PLoS One, 4(10), [e7454]. https://doi.org/10.1371/journal.pone.0007454

Empirical bayes analysis of quantitative proteomics experiments. / Margolin, Adam; Ong, Shao En; Schenone, Monica; Gould, Robert; Schreiber, Stuart L.; Carr, Steven A.; Golub, Todd R.

In: PLoS One, Vol. 4, No. 10, e7454, 14.10.2009.

Research output: Contribution to journalArticle

Margolin, A, Ong, SE, Schenone, M, Gould, R, Schreiber, SL, Carr, SA & Golub, TR 2009, 'Empirical bayes analysis of quantitative proteomics experiments', PLoS One, vol. 4, no. 10, e7454. https://doi.org/10.1371/journal.pone.0007454
Margolin A, Ong SE, Schenone M, Gould R, Schreiber SL, Carr SA et al. Empirical bayes analysis of quantitative proteomics experiments. PLoS One. 2009 Oct 14;4(10). e7454. https://doi.org/10.1371/journal.pone.0007454
Margolin, Adam ; Ong, Shao En ; Schenone, Monica ; Gould, Robert ; Schreiber, Stuart L. ; Carr, Steven A. ; Golub, Todd R. / Empirical bayes analysis of quantitative proteomics experiments. In: PLoS One. 2009 ; Vol. 4, No. 10.
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