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Techniques of Biclustering in Gene Expression Analysis

  • Anna Tamulewicz
  • Aleksandra Lipczyńska
  • Ewaryst Tkacz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)

Abstract

DNA microarrays allow to observe expression levels of large numbers of genes under different experimental conditions. One of the main goals in the gene expression analysis is clustering genes with similar expression patterns under specified conditions or clustering conditions according to expression of certain genes. Traditional clustering methods have some limitations, such as assumption that related genes behave similarly across all experimental conditions and partition of the genes into disjoint groups, which implying an association of the gene with a single biological process.

In recent years, several biclustering methods were proposed to overcome the limitations of clustering. Biclustering allows for simultaneous grouping of genes and conditions, which leads to identification of subsets of genes exhibiting similar behavior across a subset of conditions. In gene expression analysis, the term biclustering was introduced in 2000 by Cheng and Church and since then several methods were developed. The paper presents a review of the algorithmic approaches to biclustering.

Keywords

biclustering clustering gene expression analysis microarray 

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References

  1. 1.
    Ben-Dor, A., Chory, B., Karpz, R., Yakhini, Z.: Discovering local structure in gene expression data: The order-preserving submatrix problem. Journal of Computational Biology 10(3-4), 373–384 (2003)PubMedCrossRefGoogle Scholar
  2. 2.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. Int. Conf. Intell. Syst. Mol. Biol., pp. 93–103 (2000)Google Scholar
  3. 3.
    Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)Google Scholar
  4. 4.
    Ihmels, J., Bergmann, S., Barkai, N.: Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13), 1993–2003 (2004)PubMedCrossRefGoogle Scholar
  5. 5.
    Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y., Barkai, N.: Revealing modular organization in the yeast transcriptional network. Nature Genetics 31, 370–377 (2002)PubMedGoogle Scholar
  6. 6.
    Kluger, Y., Basri, R., Chang, J.T., Gerstein, M.: Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Research 13, 703–716 (2003)PubMedCrossRefGoogle Scholar
  7. 7.
    Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Statistica Sinica 12, 61–86 (2000)Google Scholar
  8. 8.
    Li, G., Ma, Q., Tang, H., Paterson, A.H., Xu, Y.: Qubic: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Research 37(15) (2009)Google Scholar
  9. 9.
    Mirkin, B.: Mathematical classification and clustering. Journal of Global Optimization 12(1), 105–108 (1996)Google Scholar
  10. 10.
    Morgan, J.N., Sonquist, J.A.: Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association 58, 415–434 (1963)Google Scholar
  11. 11.
    Murali, T.M., Kasif, S.: Extracting conserved gene expression motifs from gene expression data. In: Pac. Symp. Biocomput., pp. 77–88 (2003)Google Scholar
  12. 12.
    Perlic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefGoogle Scholar
  13. 13.
    Segal, E., Taskar, B., Gasch, A., Friedman, N., Koller, D.: Rich probabilistic models for gene expression. Bioinformatics 17(supp. 1), S243–S252 (2001)Google Scholar
  14. 14.
    Tanay, A., Sharan, R., Kupiec, M., Shamir, R.: Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proceedings of the National Academy of Sciences of the United States of America 101(9), 2981–2986 (2004)PubMedCrossRefGoogle Scholar
  15. 15.
    Tanay A., Sharan R., Shamir R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics18(supp. 1), S136–S144 (2002)Google Scholar
  16. 16.
    Turner, H., Bailey, T., Krzanowski, W.: Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics and Data Analysis 48, 235–254 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anna Tamulewicz
    • 1
  • Aleksandra Lipczyńska
    • 2
  • Ewaryst Tkacz
    • 1
  1. 1.Faculty of Biomedical Engineering, Department of Biosensors and Biomedical Signals ProcessingSilesian University of TechnologyGliwicePoland
  2. 2.Faculty of Energy and Environmental Engineering Department of Heating, Ventilation and Dust Removal TechnologySilesian University of TechnologyGliwicePoland

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