Co-regulated gene module detection for time series gene expression data

Abstract

It is important to detect interaction effect of multiple genes during certain biological process. In this paper, we proposed, from systems biology perspective, the concept of co-regulated gene module, which consists of genes that are regulated by the same regulator(s). Given a time series gene expression data, a hidden Markov model-based Bayesian model was developed to calculate the likelihood of the observed data, assuming the co-regulated gene modules are known. We further developed a Gibbs sampling strategy that is integrated with reversible jump Markov chain Monte Carlo to obtain the posterior probabilities of the co-regulated gene modules. Simulation study validated the proposed method. When compared with two existing methods, the proposed approach significantly outperformed the conventional methods.

Key words： co-regulated gene module; Bayesian; hidden Markov model; Markov chain Monte Carlo

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant Nos.60934004 and 61021063) and the Beijing excellent PhD thesis project.

References

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