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Empirical Bayes Analysis of Quantitative Proteomics Experiments
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 m...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759080/ https://www.ncbi.nlm.nih.gov/pubmed/19829701 http://dx.doi.org/10.1371/journal.pone.0007454 |
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author | Margolin, Adam A. Ong, Shao-En Schenone, Monica Gould, Robert Schreiber, Stuart L. Carr, Steven A. Golub, Todd R. |
author_facet | Margolin, Adam A. Ong, Shao-En Schenone, Monica Gould, Robert Schreiber, Stuart L. Carr, Steven A. Golub, Todd R. |
author_sort | Margolin, Adam A. |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2759080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-27590802009-10-15 Empirical Bayes Analysis of Quantitative Proteomics Experiments Margolin, Adam A. Ong, Shao-En Schenone, Monica Gould, Robert Schreiber, Stuart L. Carr, Steven A. Golub, Todd R. PLoS One Research Article 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. Public Library of Science 2009-10-14 /pmc/articles/PMC2759080/ /pubmed/19829701 http://dx.doi.org/10.1371/journal.pone.0007454 Text en Margolin et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Margolin, Adam A. Ong, Shao-En Schenone, Monica Gould, Robert Schreiber, Stuart L. Carr, Steven A. Golub, Todd R. Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title | Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title_full | Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title_fullStr | Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title_full_unstemmed | Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title_short | Empirical Bayes Analysis of Quantitative Proteomics Experiments |
title_sort | empirical bayes analysis of quantitative proteomics experiments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759080/ https://www.ncbi.nlm.nih.gov/pubmed/19829701 http://dx.doi.org/10.1371/journal.pone.0007454 |
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