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Published on: June 15, 2018
Empirical Bayes analysis of quantitative proteomics experiments.
Adam A Margolin1, Shao-En Ong, Monica Schenone
1Cancer program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. margolin@broad.mit.edu
Plos One
|October 16, 2009
Summary
This study introduces a new statistical framework for analyzing quantitative proteomics data. The method improves the accuracy of identifying protein abundance changes and challenges previous findings on microRNA regulation.
Area of Science:
- Proteomics
- Systems Biology
- Bioinformatics
Background:
- Mass spectrometry-based proteomics generates valuable data for systems biology.
- Analytical methods for proteomics data analysis require further development.
- A robust statistical framework is needed for reliable interpretation of proteomic data.
Purpose of the Study:
- To develop and present an empirical Bayes framework for quantitative proteomics data analysis.
- To provide a statistical description of experiments, including differential protein abundance, statistical power, and false-positive probabilities.
Main Methods:
- An empirical Bayes framework was developed for quantitative proteomics data analysis.
- The method was applied to analyze affinity purification experiments and microRNA-regulated protein data.
- Statistical analysis was combined with sequence analysis of 3' UTR regions.
Main Results:
- The framework precisely identified protein targets of small molecules in affinity purification experiments.
- Re-analysis of microRNA-regulated protein data revealed that a large fraction of the proteome is not regulated by microRNAs, contrary to previous conclusions.
- Sequence analysis supported the findings from the statistical analysis.
Conclusions:
- Rigorous statistical analysis is crucial for interpreting proteomic data.
- The developed empirical Bayes framework offers a robust and reliable method for quantitative proteomics data analysis.
- The study highlights the importance of re-evaluating previous conclusions with robust statistical methods.
