A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

BMC Bioinformatics

Steven H Wu¹  Michael A Black³  Robyn A North²  Allen G Rodrigo⁴ 

[1] Biology Department, Duke University, Duke Box, 90338, Durham, NC, 27708, USA

[2] Women's Health Academic Centre, King’s College London, London, UK

[3] Department of Biochemistry, University of Otago, P. O. Box 56, Dunedin, New Zealand

[4] The National Evolutionary Synthesis Center, Durham, NC, 27705, USA

关键词: Markov chain Monte Carlo (MCMC);    Differentially expressed protein;    Global Bayesian model;    Two-dimensional polyacrylamide gel electrophoresis (2D PAGE);   

Others  :  1088233
DOI  :  10.1186/1471-2105-13-137

 received in 2011-12-14, accepted in 2012-05-30,  发布年份 2012

【 摘 要 】

Background

Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection.

Results

We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression.

Conclusions

Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D webcite.

【 授权许可】

   
2012 Wu et al.; licensee BioMed Central Ltd.

【 图 表 】

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