title: Detecting Significant Changes in Protein Abundance.
abstract: 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Kammers
authors.affiliation: Mass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Robert N
authors.initials: RN
authors.lastname: Cole
authors.affiliation: 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.
authors.forename: Calvin
authors.initials: C
authors.lastname: Tiengwe
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: Ingo
authors.initials: I
authors.lastname: Ruczinski
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