Advertisement

δ-TRIMAX: Extracting Triclusters and Analysing Coregulation in Time Series Gene Expression Data

  • Anirban Bhar
  • Martin Haubrock
  • Anirban Mukhopadhyay
  • Ujjwal Maulik
  • Sanghamitra Bandyopadhyay
  • Edgar Wingender
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7534)

Abstract

In an attempt to analyse coexpression in a time series microarray gene expression dataset, we introduce here a novel, fast triclustering algorithm δ-TRIMAX that aims to find a group of genes that are coexpressed over a subset of samples across a subset of time-points. Here we defined a novel mean-squared residue score for such 3D dataset. At first it uses a greedy approach to find triclusters that have a mean-squared residue score below a threshold δ by deleting nodes from the dataset and then in the next step adds some nodes, keeping the mean squared residue score of the resultant tricluster below δ. So, the goal of our algorithm is to find large and coherent triclusters from the 3D gene expression dataset. Additionally, we have defined an affirmation score to measure the performance of our triclustering algorithm for an artificial dataset. To show biological significance of the triclusters we have conducted GO enrichment analysis. We have also performed enrichment analysis of transcription factor binding sites to establish coregulation of a group of coexpressed genes.

Keywords

Time series gene expression data Tricluster Mean-squared residue Affirmation score Gene ontology KEGG Pathway TRANSFAC 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Benjamini, Y., Yekutieli, D.: The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics 29(4), 1165–1188 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Chen, D., et al.: JunD and JunB integrate prostaglandin E2 activation of breast cancer-associated proximal aromatase promoters. Mol. Endocrinol. 25(5), 767–775 (2011)CrossRefGoogle Scholar
  4. 4.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. Int. Conf. Int. Syst. Mol. Biol., pp. 93–103 (2000)Google Scholar
  5. 5.
    Chhabra, A., et al.: Expression of transcription factor CREB1 in human breast cancer and its correlation with prognosis. Oncology Reports 18(4), 953–958 (2007)Google Scholar
  6. 6.
    Mukhopadhyay, A., et al.: A novel coherence measure for discovering scaling biclusters from gene expression data. Journal of Bioinformatics and Computational Biology 7(5), 853–868 (2009)CrossRefGoogle Scholar
  7. 7.
    Prelic, A., et al.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22, 1122–1129 (2006)CrossRefGoogle Scholar
  8. 8.
    Wingender, E., et al.: The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 29(29), 281–283 (2001)CrossRefGoogle Scholar
  9. 9.
    Boyle, E.I., et al.: GO:TermFinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 20(18), 3710–3715 (2004)CrossRefGoogle Scholar
  10. 10.
    Lanigan, F., et al.: Homeobox transcription factor muscle segment homeobox 2(Msx2) correlates with good prognosis in breast cancer patients and induces apoptosis in vitro. Breast Cancer Research 12(R59) (2010)Google Scholar
  11. 11.
    Carroll, J.S., et al.: Genome-wide analysis of estrogen receptor binding sites. Nature Genetics 38(11) (November 2006)Google Scholar
  12. 12.
    Magnani, L., et al.: PBX1 genomic pioneer function drives ERα signaling underlying progression in breast cancer. PLOS Genetics 7(11) (November 2011)Google Scholar
  13. 13.
    Fougere, M., et al.: NFAT3 transcription factor inhibits breast cancer cell motility by targeting the Lipocalin 2 gene. Oncogene 29(15), 2292–2301 (2010)CrossRefGoogle Scholar
  14. 14.
    Yoeli-Lerner, M., et al.: Akt blocks breast cancer cell motility and invasion through the transcription factor NFAT. Molecular Cell 20(4), 539–550 (2005)CrossRefGoogle Scholar
  15. 15.
    Khan, S., et al.: Role of specificity protein transcription factors in estrogeninduced gene expression in mcf-7 breast cancer cells. Journal of Molecular Endocrinology 39, 289–304 (2007)CrossRefGoogle Scholar
  16. 16.
    Tommasi, S., et al.: Methylation of homeobox genes is a frequent and early epigenetic event in breast cancer. Breast Cancer Research 11(R14) (2009)Google Scholar
  17. 17.
    Lee, S.Y., et al.: Homeobox gene Dlx-2 is implicated in metabolic stress-induced necrosis. Molecular Cancer 10(113) (2011)Google Scholar
  18. 18.
    Zhang, S.Y., et al.: E2F-1: a proliferative marker of breast neoplasia. Cancer Epidemiology, Biomarkers & Prevention 9, 395–401 (2000)Google Scholar
  19. 19.
    Maeda, T., et al.: TEF-1 transcription factors regulate activity of the mouse mammary tumor virus LTR. Biochemical and Biophysical Research Communications 296(5), 1279–1285 (2002)CrossRefGoogle Scholar
  20. 20.
    Stevens, T.A., et al.: BARX2 and estrogen receptor-alpha (ESR1) coordinately regulate the production of alternatively spliced esr1 isoforms and control breast cancer cell growth and invasion. Oncogene 25, 5426–5435 (2006)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Jauliac, S., et al.: The role of NFAT transcription factors in integrin-mediated carcinoma invasion. Nature Cell Biology 4(7), 540–544 (2002)CrossRefGoogle Scholar
  22. 22.
    Falcon, S., Gentleman, R.: Using GOstats to test gene lists for GO term association. Bioinformatics 23(2), 257–258 (2007)CrossRefGoogle Scholar
  23. 23.
    Zhao, L., Zaki, M.J.: TRICLUSTER: An effective algorithm for mining coherent clusters in 3D microarry data. In: SIGMOD (June 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anirban Bhar
    • 1
  • Martin Haubrock
    • 1
  • Anirban Mukhopadhyay
    • 2
  • Ujjwal Maulik
    • 3
  • Sanghamitra Bandyopadhyay
    • 4
  • Edgar Wingender
    • 1
  1. 1.Department of Bioinformatics, Medical SchoolGeorg August University of GoettingenGoettingenGermany
  2. 2.Department of Computer Science and EngineeringUniversity of KalyaniKalyaniIndia
  3. 3.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  4. 4.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations