RESEARCH ARTICLE

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

  • Wanwan TANG ,
  • Rui LI ,
  • Shao LI ,
  • Yanda LI
Expand
  • MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Tsinghua National Laboratory of Information Science and Technology / Department of Automation, Tsinghua University, Beijing 100084, China

Received date: 02 Jul 2012

Accepted date: 13 Aug 2012

Published date: 05 Dec 2012

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

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.

Cite this article

Wanwan TANG , Rui LI , Shao LI , Yanda LI . Co-regulated gene module detection for time series gene expression data[J]. Frontiers of Electrical and Electronic Engineering, 2012 , 7(4) : 357 -366 . DOI: 10.1007/s11460-012-0207-x

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.
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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI PMID

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI PMID

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

DOI PMID

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

DOI PMID

Options
Outlines

/