Skip to main content

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 modelbased 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.

This is a preview of subscription content, access via your institution.

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    MathSciNet  MATH  Article  Google Scholar 

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

    MathSciNet  MATH  Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    MathSciNet  Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wanwan Tang.

About this article

Cite this article

Tang, W., Li, R., Li, S. et al. Co-regulated gene module detection for time series gene expression data. Front. Electr. Electron. Eng. 7, 357–366 (2012). https://doi.org/10.1007/s11460-012-0207-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11460-012-0207-x

Keywords

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