|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Margolin, AA, Ong, S-E, Schenone, M, Gould, R, Schreiber, SL, Carr, SA, Golub, TR|
|Date Published||2009 Oct 14|
|Keywords||3' Untranslated Regions, Bayes Theorem, Computational Biology, False Positive Reactions, HeLa Cells, Humans, Mass Spectrometry, Models, Statistical, Normal Distribution, Proteome, Proteomics, Reproducibility of Results, Sequence Analysis, Protein|
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.
|Alternate Journal||PLoS ONE|
|PubMed Central ID||PMC2759080|
|Grant List||RL1 GM 084437 / GM / NIGMS NIH HHS / United States |
UL1 RR 024924 / RR / NCRR NIH HHS / United States
UL1 RR024924 / RR / NCRR NIH HHS / United States
RL1 GM084437 / GM / NIGMS NIH HHS / United States
RL1 CA 133834 / CA / NCI NIH HHS / United States
RL1 CA133834 / CA / NCI NIH HHS / United States