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EuPA Open Proteom. 2015 Jun;7:11-19.

Detecting Significant Changes in Protein Abundance.

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Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Mass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ; Department of Microbiology and Immunology, School of Medicine and Biomedical Sciences, University at Bu alo, Bu alo, NY, USA.


We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labeled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.

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