Skip to main content
PMC home page
Journal of Biological Physics logoLink to Journal of Biological Physics
. 2002 Sep;28(3):449–464. doi: 10.1023/A:1020337311471

System for Automatically Inferring a Genetic Netwerk from Expression Profiles

H Toh 1, K Horimoto 2
PMCID: PMC3456747  PMID: 23345788

Abstract

A system is constructed to automatically infer a genetic network byapplication of graphical Gaussian modeling to the expression profiledata. Our system is composed of two parts: one part is automaticdetermination of cluster boundaries of profiles in hierarchicalclustering, and another part is inference of a genetic network byapplication of graphical Gaussian modeling to the clustered profiles.Since thousands of or tens of thousands of gene expression profiles aremeasured under only one hundred conditions, the profiles naturally showsome similar patterns. Therefore, a preprocessing for systematicallyclustering the profiles is prerequisite to infer the relationship betweenthe genes. For this purpose, a method for automatic determination ofcluster boundaries is newly developed without any biological knowledgeand any additional analyses. Then, the profiles for each cluster areanalyzed by graphical Gaussian modeling to infer the relationship betweenthe clusters. Thus, our system automatically provides a graph betweenclusters only by input the profile data. The performance of the presentsystem is validated by 2467 profiles from yeast genes. The clusters andthe genetic network obtained by our system are discussed in terms of thegene function and the known regulatory relationship between genes.

Keywords: cluster analysis, cluster boundary, gene expression profile, genetic network, graphical Gaussian mmodeling, microarray

Full Text

The Full Text of this article is available as a PDF (125.7 KB).

References

  • 1.Lockhart D.J., Dong H., Byrne M.C., Follettie M.T., Gallo M.V., Chee M.S., Mittmann M., Wang C., Kobayashi M., Horton H., Brown E.L. DNA expression monitoring by hybridization to high density oligonucleotide arrays. Nature Biotechnol. 1996;14:1657–1680. doi: 10.1038/nbt1296-1675. [DOI] [PubMed] [Google Scholar]
  • 2.Shalon D., Smith S.J., Brown P.O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 1996;6:639–645. doi: 10.1101/gr.6.7.639. [DOI] [PubMed] [Google Scholar]
  • 3.DeRisi J., Iyer V., Brown P. Exploring the metabolic genetic control of gene expression on a genomic scale. Science. 1997;278:680–686. doi: 10.1126/science.278.5338.680. [DOI] [PubMed] [Google Scholar]
  • 4.Spellman P.T., Sherlock G., Zhang M.Q., Iyer V.R., Anders K., Eisen M.B., Brown P.O., Botstein D., Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Sacchromyces cerevisiaeby microarray hybridization. Mol. Biol. Cell. 1998;9:3273–3297. doi: 10.1091/mbc.9.12.3273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA. 1998;95:14863–14868. doi: 10.1073/pnas.95.25.14863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wen X., Fuhrman S., Michaels G.S., Carr D.B., Smith S., Barker J.L., Somogyi R. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. USA. 1998;95:334–339. doi: 10.1073/pnas.95.1.334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Alon U., Barkai N., Notterman D.A., Gish G., Ybarra S., Mack D., Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA. 1999;96:6745–6750. doi: 10.1073/pnas.96.12.6745. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Ben-Dor A., Shamir R., Yakhini Z. Clustering gene expression patterns. J. Comput. Biol. 1999;6:281–297. doi: 10.1089/106652799318274. [DOI] [PubMed] [Google Scholar]
  • 9.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. Proc. Natl. Acad. Sci. USA. 1999;96:2907–2912. doi: 10.1073/pnas.96.6.2907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Tavazoie S., Hughes J.D., Campbell M.J., Cho R.J., Church G.M. Systematic determination of genetic network architecture. Nature Genet. 1999;22:281–285. doi: 10.1038/10343. [DOI] [PubMed] [Google Scholar]
  • 11.Eisen M.B., Brown P.O. DNA arrays for analysis of gene expression. Methods Enzymol. 1999;303:179–205. doi: 10.1016/s0076-6879(99)03014-1. [DOI] [PubMed] [Google Scholar]
  • 12.Vilo J., Brazma A., Jonassen I., Robinson A., Ukkonen E. Mining for putative regulatory elements in the yeast genome using gene expression data. In: Altman R., Bailey T.L., Bourne P., Gribskov M., Lengauer T., Shindyalov I.N., Ten Eyck L.F., Weissig H., editors. Proceedings of Eighth International Conference on Intelligent Systems for molecular Biology. Menlo Park: AAAI Press; 2000. pp. 384–394. [PubMed] [Google Scholar]
  • 13.Hartigan J.A. Clustering Algorithms. New York: Wiley; 1975. [Google Scholar]
  • 14.Gordon A.D. Classification. London: Chapman and Hall; 1981. [Google Scholar]
  • 15.Somogyi R., Shiegoski C.A. Modeling the complexity of genetic networks: Understanding multigene and pleiotropic regulation. Complexity. 1996;1:45–63. [Google Scholar]
  • 16.Chen, T., He, H.L. and Church, G.M.: Modeling gene expression with differential equations, Proc. Pacific Symp. Biocomput.(1999), 17–28. [PubMed]
  • 17.D'Haeseleer, P., Wen, X., Fuhrman, S. and Somogyi, R.: Linear modeling of mRNA expression levels during CNS development and injury, Proc. Pacific Symp. Biocomput.(1999), 41–52. [DOI] [PubMed]
  • 18.Akutsu, T., Miyano, S. and Kuhara, S.: Algorithms for inferring qualitative models of biological networks, Proc. Pacific Symp. Biocomput.(2000), 290–301. [DOI] [PubMed]
  • 19.Friedman N., Linial M., Nachman I., Pe'er D. Using Bayesian networks to analyze expression data. J Comp. Biol. 2000;7:601–620. doi: 10.1089/106652700750050961. [DOI] [PubMed] [Google Scholar]
  • 20.Toh, H. and Horimoto, K.: Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling, Bioinformatics, in press. [DOI] [PubMed]
  • 21.Horimoto K., Toh H. Statistical estimation of cluster boundaries in gene expression profile data. Bioinformatics. 2001;17:1143–1151. doi: 10.1093/bioinformatics/17.12.1143. [DOI] [PubMed] [Google Scholar]
  • 22.Whittaker J. Graphical Models in Applied Multivariate Statistics. Chichester: John Wiley; 1990. [Google Scholar]
  • 23.Edwards D. Introduction to Graphical Modelling. Second Edition. New York: Springer; 2000. [Google Scholar]
  • 24.Sneath P.H.A., Sokal R.R. Numerical Taxonomy. San Francisco: W.H. Freeman and Company; 1973. [Google Scholar]
  • 25.Freund R.J., Wilson W.J. Regression Analysis. San Diego: Academic Press; 1998. [Google Scholar]
  • 26.Chatterjee S., Price B. Regression Analysis by Examples. New York: John Wiley & Sons; 1977. [Google Scholar]
  • 27.Wermuth N., Scheidt E. Fitting a covariance selection to a matrix. Algorithm AS 105. Appl. Statist. 1977;26:88–92. [Google Scholar]
  • 28.Cherry J.M., Ball C., Weng S., Juvik G., Schmidt R., Adler C., Dunn B., Dwight S., Riles L., Mortimer R.K., et al. Genetic and physical maps of Saccharomyces cerevisiae. Nature. 1997;387:67–73. [PMC free article] [PubMed] [Google Scholar]
  • 29.Mewes H.W., Frishman D., Gruber C., Geier B., Haase D., Kaps A., Lemcke K., Mannhaupt G., Pfeiffer F., Schuller C., et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 2000;28:37–40. doi: 10.1093/nar/28.1.37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Peterson C.L., Herskowitz I. Characterization of the Yeast SWI1, SEI1, and SWI3 Genes, which encode a global activator of transcription. Cell. 1992;68:573–583. doi: 10.1016/0092-8674(92)90192-f. [DOI] [PubMed] [Google Scholar]
  • 31.Li Y., Bjorklund S., Jiang Y.W., Kim Y.J., Lane W.S., Stillman D.J., Kornberg R.D. Yeast global transcriptional regulators Sin4 and Rgr1 are component of mediator complex/RNA polymerase II holoenzyme. Proc. Natl. Acad. Sci. USA. 1995;92:10864–10868. doi: 10.1073/pnas.92.24.10864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tzamarias D., Struhl K. Functional dissection of the yeast Cyc8-Tup1 transcriptional co-repressor complex. Nature. 1994;369:758–761. doi: 10.1038/369758a0. [DOI] [PubMed] [Google Scholar]
  • 33.Efron B., Gong G. A leisurely look at the bootstrap, the jackknife and crossvalidation. Amer. Statistician. 1982;37:36–48. [Google Scholar]

Articles from Journal of Biological Physics are provided here courtesy of Springer Science+Business Media B.V.

RESOURCES