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.

Keywords

co-regulated gene module / Bayesian / hidden Markov model / Markov chain Monte Carlo

References

[1] Mootha V K, Lindgren C M, Eriksson K F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly M J, Patterson N, Mesirov J P, Golub T R, Tamayo P, Spiegelman B, Lander E S, Hirschhorn J N, Altshuler D, Groop L C. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 2003, 34(3): 267–273

[2] Hartwell L H, Hopfield J J, Leibler S, Murray A W. From molecular to modular cell biology. Nature, 1999, 402(6761 Suppl): C47–C52

[3] Wang L, Zhang B, Wolfinger R D, Chen X. An integrated approach for the analysis of biological pathways using mixed models. PLoS Genetics, 2008, 4(7): e1000115

[4] Gu J, Chen Y, Li S, Li Y. Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis. BMC Systems Biology, 2010, 4(1): 47

[5] Eisen M B, Spellman P T, Brown P O, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 1998, 95(25): 14863–14868

[6] Tavazoie S, Hughes J D, Campbell M J, Cho R J, Church G M. Systematic determination of genetic network architecture. Nature Genetics, 1999, 22(3): 281–285

[7] Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E S, Golub T R. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences of the United States of America, 1999, 96(6): 2907–2912

[8] Carter S L, Brechbühler C M, Griffin M, Bond A T. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics, 2004, 20(14): 2242–2250

[9] Davidson G S, Wylie B N, Boyack K W. Cluster stability and the use of noise in interpretation of clustering. In: Proceedings of IEEE Symposium on Information Visualization 2001. 2001, 23–30

[10] Elo L L, Järvenpää H, Oresic M, Lahesmaa R, Aittokallio T. Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics, 2007, 23(16): 2096–2103

[11] Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, 77(2): 257–286

[12] Baum L E, Petrie T, Soules G, Weiss N. A Maximization Technique Occurring in Statistical Analysis of Probabilistic Functions of Markov Chains. Annals of Mathematical Statistics, 1970, 41(1): 164–171

[13] Green P J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 1995, 82(4): 711–732

[14] Strauss D J. Clustering Algorithms — Hartigan, JA. Biometrics, 1975, 31(3): 793

[15] Oldham M C, Horvath S, Geschwind D H. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(47): 17973–17978

[16] Horvath S, Zhang B, Carlson M, Lu K V, Zhu S, Felciano R M, Laurance M F, Zhao W, Qi S, Chen Z, Lee Y, Scheck A C, Liau L M, Wu H, Geschwind D H, Febbo P G, Kornblum H I, Cloughesy T F, Nelson S F, Mischel P S. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(46): 17402–17407

[17] Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 2005, 4: Article17

[18] Tang W, Wu X, Jiang R, Li Y. Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genetics, 2009, 5(5): e1000464

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.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg