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

TL;DR

This paper introduces a Bayesian statistical method for analyzing multi-group isobaric labelled mass spectrometry data to identify differentially expressed proteins across multiple experiments with improved accuracy.

Contribution

A novel Bayesian inference approach that models reporter ion intensities across groups and experiments for differential protein expression analysis.

Findings

Method performs well on simulated data.

Accurate identification of differentially expressed proteins in experimental datasets.

Provides a unified probabilistic framework for multi-group proteomic analysis.

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.