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

Author

Listed:
  • Jow Howsun

    (School of Mathematics & Statistics, Newcastle University, UK)

  • Boys Richard J.

    (School of Mathematics & Statistics, Newcastle University, UK)

  • Wilkinson Darren J.

    (School of Mathematics & Statistics, Newcastle University, UK)

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.

Suggested Citation

  • Jow Howsun & Boys Richard J. & Wilkinson Darren J., 2014. "Bayesian identification of protein differential expression in multi-group isobaric labelled mass spectrometry data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 531-551, October.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:5:p:21:n:2
    DOI: 10.1515/sagmb-2012-0066
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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