A Comparative Study of Clustering Methods for Relevant Gene Selection in Microarray Data

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

Classification of microarray cancer data has drawn the attention of research community for better clinical diagnosis in last few years. Microarray datasets are characterized by high dimension and small sample size. Hence, the conventional wrapper methods for relevant gene selection cannot be applied directly on such datasets due to large computation time. In this paper, a two stage approach is proposed to determine a subset containing relevant and non redundant genes for better classification of microarray data. In first stage, genes were partitioned into distinct clusters to identify redundant genes. To determine the better choice of clustering algorithm to group redundant genes, four different clustering methods were investigated. Experiments on four well known cancer microarray datasets depicted that hierarchical agglomerative with complete link approach performed the best in terms of average classification accuracy for three datasets. Comparison with other state-of-art methods have shown that the proposed approach which involves gene clustering is effective in reducing redundancy among selected genes to provide better classification.

Keywords

Clustering Microarray Gene Selection Representative Entropy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bellman, R.: Adaptive Control Processes. In: A Guided Tour. Princeton University Press (1961)Google Scholar
  2. 2.
    Guyon, I., Elisseeff, A.: An Introduction to Variable and feature Selection. Journal of Machine Learning Research (3), 1157–1182 (2003)Google Scholar
  3. 3.
    Jain, A.K., Murthy, M.K., Flynn, P.J.: Data Clustering: A Review ACM Computing surveys, vol. 31, pp. 264–318Google Scholar
  4. 4.
    Rui, X., Wunsch, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3) (2005)Google Scholar
  5. 5.
    Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  6. 6.
    Yang, K., Cai, Z., Li, J., Lin, G.H.: A stable gene selection in microarray data analysis. BMC Bioinformatics (2006) 1471-2105-7-228Google Scholar
  7. 7.
    Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97(457), 77–87 (2002)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)CrossRefMATHGoogle Scholar
  9. 9.
    Tseng, G.C., Wong, W.H.: Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data Biometrics, vol. 61, pp. 10–16 (2005)Google Scholar
  10. 10.
    Au, W.H., Chan, K.C., Wong, A.K., Wang, Y.: Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 2(2), 83–101 (2005)CrossRefGoogle Scholar
  11. 11.
    Cai, Z., Xu, L., et al.: Using clustering to identify discriminatory genes with higher classification accuracy. In: IEEE Symposium 0-7695-2727-2/06Google Scholar
  12. 12.
    Mukhopadhyay, A., et al.: Simultaneous Informative Gene Selection and Clustering through Multiobjective Optimization. IEEE Congress on Evol. Comp., 1–8 (2010)Google Scholar
  13. 13.
    Tavazoie, S., Huges, D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nature Genet., 281–285 (1999)Google Scholar
  14. 14.
    Eisen, M.B., Spellman, T.P., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. USA 95(25), 14863–14868 (1998)CrossRefGoogle Scholar
  15. 15.
    Pal, S.K., Mitra, P.: Pattern recognition algorithms for data mining. Chap. and Hall (2008)Google Scholar
  16. 16.
    Bala, R., Agrawal, R.K., Sardana, M.: Relevant Gene Selection Using Normalized Cut Clustering with Maximal Compression Similarity Measure. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part II. LNCS, vol. 6119, pp. 81–88. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Kohonen, T.: Self-organizing maps. Springer, Berlin (1995)CrossRefGoogle Scholar
  18. 18.
    Su, A.I., Welsh, J.B.: Molecular classification of human carcinomas by gene expression signatures. Cancer Research 61, 7388–7393 (2001)Google Scholar
  19. 19.
    Ramaswamy, S., Tamayo, P., Rifkin, R., et al.: Multi-class cancer diagnosis using tumor gene expression signatures. PNAS 98, 15149–15154 (2001), Dataset description CrossRefGoogle Scholar
  20. 20.
    Ross, D.T., Scherf, U., Eisen, M.B., Perou, C.M., et al.: Systematic Variation in Gene Expression Patterns in Human Cancer Cell Lines. Nature Genet. 24, 227–235 (2000)CrossRefGoogle Scholar
  21. 21.
    Alon, U., Barkai, N., et al.: Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Array. Proc. Nat’l Academy of Science 96(12), 6745–6750 (1999)CrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Ding, C., Li, T.: Gene selection algorithm by combining Relief and RMR. BMC Genomics 9(2), S27 (2008)CrossRefGoogle Scholar
  23. 23.
    Wang, L., Chu, F., Xie, W.: Accurate cancer classification using expressions of very few genes. IEEE/ACM Trans. on Comp. Biology and Bioinformatics 4(1) (2007)Google Scholar
  24. 24.
    Cho, J., Lee, D., Park, J.H., Lee, I.B.: New gene selection for classification of cancer subtype considering within-class variation. FEBS Letters 551, 3–7 (2003)CrossRefGoogle Scholar
  25. 25.
    Zhu, Z., Ong, Y.-S., Monoranjan, D.: Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework. IEEE Trans. Cybernatics 37(1) (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer and System SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations