Frontiers of Electrical and Electronic Engineering >
Co-regulated gene module detection for time series gene expression data
Received date: 02 Jul 2012
Accepted date: 13 Aug 2012
Published date: 05 Dec 2012
Copyright
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.
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
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
|
/
〈 | 〉 |