Advertisement

Inferring Transcriptional Modules from Microarray and ChIP-Chip Data Using Penalized Matrix Decomposition

  • Chun-Hou Zheng
  • Wen Sha
  • Zhan-Li Sun
  • Jun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

Abstract

Inferring transcriptional regulatory modules is a useful work for elucidating molecular mechanism. In this paper, we propose a new method for transcriptional regulatory module discovering. The algorithm uses penalized matrix decomposition to model microarray data. Which takes into account the sparse a prior information of transcription factors–gene (TFs–gene) interactions. At the same time, the ChIP-chip data are used as constraints for penalized matrix decomposition of gene expression data. Finally the regulatory modules can be inferred based on the factor matrix. Experiment on yeast dataset shows that our method can identifies more meaningful transcriptional modules relating to specific TFs.

Keywords

Transcriptional modules penalized matrix decomposition gene expression data ChIP-chip data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cavalieri, D., De Filippo, C.: Bioinformatic Methods for Integrating Whole-Genome Expression Results Into Cellular Networks. Drug Discov. Today 10, 727–734 (2005)CrossRefGoogle Scholar
  2. 2.
    The Cancer Genome Atlas Network.: Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012)Google Scholar
  3. 3.
    Segal, E., et al.: A Module Map Showing Conditional Activity of Expression Modules in Cancer. Nat. Genet. 36, 1090–1098 (2004)CrossRefGoogle Scholar
  4. 4.
    Mukhopadhyay, A., Maulik, U.: Towards Improving Fuzzy Clustering Using Support Vector Machine: Application to Gene Expression Data. Pattern Recognition 42(11), 2744–2763 (2009)zbMATHCrossRefGoogle Scholar
  5. 5.
    Fernandez, E.A., Balzarini, M.: Improving Cluster Visualization in Self-Organizing Maps: Application in Gene Expression Data Analysis. Computers in Biology and Medicine 37(12), 1677–1689 (2007)CrossRefGoogle Scholar
  6. 6.
    Dueck, D., Morris, Q.D., Frey, B.J.: Multi-way Clustering of Microarray Data Using Probabilistic Sparse Matrix Factorization. Bioinformatics 21(suppl. 1), i144–i151 (2005)CrossRefGoogle Scholar
  7. 7.
    Huang, D.S., Zheng, C.H.: Independent Component Analysis Based Penalized Discriminant Method for Tumor Classification using Gene Expression Data. Bioinformatics 22(15), 1855–1862 (2006)CrossRefGoogle Scholar
  8. 8.
    Zhou, X.J., et al.: Functional Annotation and Network Reconstruction Through Cross-Platform Integration of Microarray Data. Nat. Biotechnol. 23, 238–243 (2005)CrossRefGoogle Scholar
  9. 9.
    Liao, J.C., Boscolo, R., Yang, Y.L., Tran, L.M., Sabatti, C., Roychowdhury, V.P.: Network Component Analysis: Reconstruction of Regulatory Signals In Biological Systems. Proc. Natl. Acad. Sci. USA 100, 15522–15527 (2003)CrossRefGoogle Scholar
  10. 10.
    Van den Bulcke, T., Lemmens, K., Van de Peer, Y., Marchal, K.: Inferring Transcriptional Networks by Mining ’Omics’ Data. Current Bioinformatics 1(3), 301–313 (2006)CrossRefGoogle Scholar
  11. 11.
    Bar-Joseph, Z., et al.: Computational Discovery of Gene Modules And Regulatory Networks. Nat. Biotechnol. 21, 1337–1342 (2003)CrossRefGoogle Scholar
  12. 12.
    Chen, G., et al.: Clustering of Genes Into Regulons using Integrated Modeling-COGRIM. Genome Biol. 8, R4 (2007)CrossRefGoogle Scholar
  13. 13.
    Lemmens, K., et al.: Inferring Transcriptional Modules from Chip-Chip, Motif and Microarray Data. Genome Biol. 7, R37 (2006)CrossRefGoogle Scholar
  14. 14.
    Zhang, J., Zheng, C.H., Liu, J.X., Wang, H.: Discovering the Transcriptional Modules using Microarray Data by Penalized Matrix Decomposition. Computers in Biology and Medicine 41(11), 1041–1050 (2011)CrossRefGoogle Scholar
  15. 15.
    Witten, D.M., Tibshirani, R., Hastie, T.: A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis. Biostatistics 10(3), 515–534 (2009)CrossRefGoogle Scholar
  16. 16.
    Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., et al.: Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Saccharomyces Cerevisiae by Microarray Hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)CrossRefGoogle Scholar
  17. 17.
    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, 4241–4257 (2000)CrossRefGoogle Scholar
  18. 18.
    Troyanskaya, O., Canto’r, M.: Missing Value Estimation Methods for DNA Microarrays. Bioinformatics 17, 520–525 (2001)CrossRefGoogle Scholar
  19. 19.
    Harbison, C.T., et al.: Transcriptional Regulatory Code of a Eukaryotic Genome. Nature 431, 99–104 (2004)CrossRefGoogle Scholar
  20. 20.
    Boyle, E.I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J.M., Sherlock, G.: 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chun-Hou Zheng
    • 1
  • Wen Sha
    • 1
  • Zhan-Li Sun
    • 1
  • Jun Zhang
    • 1
  1. 1.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina

Personalised recommendations