Detecting significant changes in protein abundance

Detecting significant changes in protein abundance

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 labelled p...

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Bibliographic Details
Main Authors: Kai Kammers, Robert N. Cole, Calvin Tiengwe, Ingo Ruczinski
Format: Article
Language:English
Published: Elsevier 2015-06-01
Series:EuPA Open Proteomics
Subjects:
Empirical Bayes
Inference
Protein abundance
Online Access:http://www.sciencedirect.com/science/article/pii/S2212968515000069
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spelling doaj-29039cbd896f404ca56c59dd0a86aee22020-11-24T23:31:57ZengElsevierEuPA Open Proteomics2212-96852015-06-017C111910.1016/j.euprot.2015.02.002Detecting significant changes in protein abundanceKai Kammers0Robert N. Cole1Calvin Tiengwe2Ingo Ruczinski3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAMass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartment of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAWe 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 labelled 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.http://www.sciencedirect.com/science/article/pii/S2212968515000069Empirical BayesInferenceProtein abundance
collection DOAJ
language English
format Article
sources DOAJ
author Kai Kammers
Robert N. Cole
Calvin Tiengwe
Ingo Ruczinski
spellingShingle Kai Kammers
Robert N. Cole
Calvin Tiengwe
Ingo Ruczinski
Detecting significant changes in protein abundance
EuPA Open Proteomics
Empirical Bayes
Inference
Protein abundance
author_facet Kai Kammers
Robert N. Cole
Calvin Tiengwe
Ingo Ruczinski
author_sort Kai Kammers
title Detecting significant changes in protein abundance
title_short Detecting significant changes in protein abundance
title_full Detecting significant changes in protein abundance
title_fullStr Detecting significant changes in protein abundance
title_full_unstemmed Detecting significant changes in protein abundance
title_sort detecting significant changes in protein abundance
publisher Elsevier
series EuPA Open Proteomics
issn 2212-9685
publishDate 2015-06-01
description 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 labelled 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.
topic Empirical Bayes
Inference
Protein abundance
url http://www.sciencedirect.com/science/article/pii/S2212968515000069
work_keys_str_mv AT kaikammers detectingsignificantchangesinproteinabundance
AT robertncole detectingsignificantchangesinproteinabundance
AT calvintiengwe detectingsignificantchangesinproteinabundance
AT ingoruczinski detectingsignificantchangesinproteinabundance
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