Biclustering of Time Series Microarray Data

  • Jia Meng
  • Yufei HuangEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


Clustering is a popular data exploration technique widely used in microarray data analysis. In this chapter, we review ideas and algorithms of bicluster and its applications in time series microarray analysis. We introduce first the concept and importance of biclustering and its different variations. We then focus our discussion on the popular iterative signature algorithm (ISA) for searching biclusters in microarray dataset. Next, we discuss in detail the enrichment constraint time-dependent ISA (ECTDISA) for identifying biologically meaningful temporal transcription modules from time series microarray dataset. In the end, we provide an example of ECTDISA application to time series microarray data of Kaposi’s Sarcoma-associated Herpesvirus (KSHV) infection.

Key words

Time series Clustering Bicluster Iterative signature algorithm Temporal module Microarray Time dependent Enrichment constrained 



This work is supported by an NSF Grant CCF-0546345.


  1. 1.
    Spellman PT, Sherlock G, Zhang MQ et al (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9:3273–3297.PubMedGoogle Scholar
  2. 2.
    Fuhrken PG, Chen C, Miller WM et al (2007) Comparative, genome-scale transcriptional analysis of CHRF-288-11 and primary human megakaryocytic cell cultures provides novel insights into lineage-specific differentiation. Exp Hematol 35:476–489.PubMedCrossRefGoogle Scholar
  3. 3.
    Eisen MB, Spellman PT, Brown PO et al (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868.PubMedCrossRefGoogle Scholar
  4. 4.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. p 14. California, USA.Google Scholar
  5. 5.
    Tamayo P, Slonim D, Mesirov J et al (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A 96:2907–2912.PubMedCrossRefGoogle Scholar
  6. 6.
    Alon U, Barkai N, Notterman DA et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 96:6745–6750.PubMedCrossRefGoogle Scholar
  7. 7.
    Bittner M, Meltzer P, Trent J (1999) Data analysis and integration: of steps and arrows. Nat Genet 22:213–215.PubMedCrossRefGoogle Scholar
  8. 8.
    Cheng Y, Church GM (2000) Biclustering of expression data. Proc Int Conf Intell Syst Mol Biol 8:93–103.PubMedGoogle Scholar
  9. 9.
    Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 97:12079–12084.PubMedCrossRefGoogle Scholar
  10. 10.
    Ihmels J, Friedlander G, Bergmann S et al (2002) Revealing modular organization in the yeast transcriptional network. Nat Genet 31:370–377.PubMedGoogle Scholar
  11. 11.
    Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 1:24–45.PubMedCrossRefGoogle Scholar
  12. 12.
    Bergmann S, Ihmels J, Barkai N (2003) Iterative signature algorithm for the analysis of large-scale gene expression data. Phys Rev E Stat Nonlin Soft Matter Phys 67:031902.Google Scholar
  13. 13.
    Kloster M (2004) Self-organized criticality, competitive evolution and analysis of gene-expression data. Ph.D. Dissertation. Department of Physics, Princeton University.Google Scholar
  14. 14.
    Supper J, Strauch M, Wanke D et al (2007) EDISA: extracting biclusters from multiple time-series of gene expression profiles. BMC Bioinformatics 8:334.PubMedCrossRefGoogle Scholar
  15. 15.
    Meng J, Gao S, Huang Y (2009) Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules. Bioinformatics 25:1521–1527.PubMedCrossRefGoogle Scholar
  16. 16.
    Ashburner M, Ball C, Blake J et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics 25:25–29.Google Scholar
  17. 17.
    Kanehisa M, Araki M, Goto S et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36: D480–484.PubMedCrossRefGoogle Scholar
  18. 18.
    Krupa S, Anthony K, Buchoff J et al (2007) The NCI-Nature Pathway Interaction Database: A cell signaling resource. Nature Preceedings.  10.1038/npre.2007.1311.1.
  19. 19.
    Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550.PubMedCrossRefGoogle Scholar
  20. 20.
    Gao SJ, Deng JH, Zhou FC (2003) Productive lytic replication of a recombinant Kaposi’s sarcoma-associated herpesvirus in efficient primary infection of primary human endothelial cells. J Virol 77:9738–9749.PubMedCrossRefGoogle Scholar
  21. 21.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Texas at San AntonioSan AntonioUSA
  2. 2.Greehey Children’s Cancer Research InstituteUniversity of Texas Health Science Center at San AntonioSan AntonioUSA

Personalised recommendations