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Bayesian identification of protein differential expression in multi-group isobaric labelled mass spectrometry data

Lookup NU author(s): Dr Howsun Jow, Professor Richard Boys, Professor Darren Wilkinson

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Abstract

In this paper we develop a Bayesian statistical inference approach to the unified analysis of isobaric labelled MS/MS proteomic data across multiple experiments. An explicit probabilistic model of the log-intensity of the isobaric labels' reporter ions across multiple pre-defined groups and experiments is developed. This is then used to develop a full Bayesian statistical methodology for the identification of differentially expressed proteins, with respect to a control group, across multiple groups and experiments. This methodology is implemented and then evaluated on simulated data and on two model experimental datasets (for which the differentially expressed proteins are known) that use a TMT labelling protocol.

Publication metadata

Author(s): Jow H, Boys RJ, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Statistical Applications in Genetics and Molecular Biology

Year: 2014

Volume: 13

Issue: 5

Pages: 531-551

Print publication date: 01/10/2014

Online publication date: 23/08/2014

ISSN (print): 2194-6302

ISSN (electronic): 1544-6115

Publisher: Walter de Gruyter GmbH

URL: http://dx.doi.org/10.1515/sagmb-2012-0066

DOI: 10.1515/sagmb-2012-0066

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