Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network

  • Jianlong Qi
  • Tom Michoel
  • Gregory Butler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6623)


The module network method has been widely used to infer transcriptional regulatory network from gene expression data. A common strategy of module network learning algorithms is to apply regression trees to infer the regulation program of a module. In this work we propose to apply linear models to fulfill this task. The novelty of our method is to extract the contrast in which a module’s genes are most significantly differentially expressed. Consequently, the process of learning the regulation program for the module becomes one of identifying transcription factors that are also differentially expressed in this contrast. The effectiveness of our algorithm is demonstrated by the experiments in a yeast benchmark dataset.


Regression Tree Condition Cluster Module Network Regulation Program Regulatory Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nitrogen regulation in saccharomyces cerevisiae. Gene 290(1-2), 1–18 (2002)Google Scholar
  2. 2.
    Cunningham, T.S., Rai, R., Cooper, T.G.: The Level of DAL80 Expression Down-Regulates GATA Factor-Mediated Transcription in Saccharomyces cerevisiae. J. Bacteriol. 182(23), 6584–6591 (2000)CrossRefGoogle Scholar
  3. 3.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  4. 4.
    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 95(25), 14863–14868 (1998)CrossRefGoogle Scholar
  5. 5.
    Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J.J., Gardner, T.S.: Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biology 5(1), 54–66 (2007)CrossRefGoogle Scholar
  6. 6.
    Friedman, N.: Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303(5659), 799–805 (2004)CrossRefGoogle Scholar
  7. 7.
    Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. Mol. Biol. Cell 11(12), 4241–4257 (2000)CrossRefGoogle Scholar
  8. 8.
    Joshi, A., De Smet, R., Marchal, K., Van de Peer, Y., Michoel, T.: Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 25(4), 490–496 (2009)CrossRefGoogle Scholar
  9. 9.
    Joshi, A., Van de Peer, Y., Michoel, T.: Analysis of a Gibbs sampler method for model-based clustering of gene expression data. Bioinformatics 24(2), 176–183 (2008)CrossRefGoogle Scholar
  10. 10.
    Kutner, M.H., Neter, J., Nachtsheim, C.J., Li, W.: Applied Linear Statistical Models. McGraw-Hill Irwin, New York (2005)Google Scholar
  11. 11.
    Li, J., Liu, Z.J., Pan, Y.C., Liu, Q., Fu, X., Cooper, N.G., Li, Y., Qiu, M., Shi, T.: Regulatory module network of basic/helix-loop-helix transcription factors in mouse brain. Genome Biol. 8(11), R244 (2007)CrossRefGoogle Scholar
  12. 12.
    Monteiro, P.T., Mendes, N.D., Teixeira, M.C., d’Orey, S., Tenreiro, S., Mira, N.P., Pais, H., Francisco, A.P., Carvalho, A.M., Lourenco, A.B., Sa-Correia, I., Oliveira, A.L., Freitas, A.T.: YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae. Nucl. Acids Res. 36(suppl. 1), 132–136 (2008)Google Scholar
  13. 13.
    Qi, J., Michoel, T., Butler, G.: A regression tree-based gibbs sampler to learn the regulation programs in a transcription regulatory module network. In: Proceedings of 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–8 (2010)Google Scholar
  14. 14.
    Segal, E., Pe’er, D., Regev, A., Koller, D., Friedman, N.: Learning module networks. Journal of Machine Learning Research 6, 557–588 (2005)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34(2), 166–176 (2003)CrossRefGoogle Scholar
  16. 16.
    Smyth, G.K.: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3 (2004)Google Scholar
  17. 17.
    Smyth, G.K.: Bioinformatics and Computational Biology Solutions using R and Bioconductor, pp. 397–420. Springer, New York (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jianlong Qi
    • 1
  • Tom Michoel
    • 2
  • Gregory Butler
    • 1
  1. 1.Department of Computer ScienceConcordia UniversityMontrealCanada
  2. 2.Freiburg Institute for Advanced StudiesSchool of Life Sciences - LifeNetFreiburgGermany

Personalised recommendations