Multiclass Microarray Gene Expression Analysis Based on Mutual Dependency Models

  • Girija Chetty
  • Madhu Chetty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)


In this paper a novel feature selection technique based on mutual dependency modelling between genes is proposed for multiclass microarray gene expression classification. Several studies on analysis of gene expression data has shown that the genes (whether or not they belong to the same gene group) get co-expressed via a variety of pathways. Further, a gene may participate in multiple pathways that may or may not be co-active for all samples. It is therefore biologically meaningful to simultaneously divide genes into functional groups and samples into co-active categories. This can be done by modeling gene profiles for multiclass microarray gene data sets based on mutual dependency models, which model complex gene interactions. Most of the current works in multiclass microarray gene expression studies are based on statistical models with little or no consideration of gene interactions. This has led to lack of robustness and overly optimistic estimates of accuracy and noise reduction. In this paper, we propose multivariate analysis techniques which model the mutual dependency between the features and take into account complex interactions for extracting a subset of genes. The two techniques, the cross modal factor analysis (CFA) and canonical correlation analysis(CCA) show a significant reduction in dimensionality and class-prediction error, and improvement in classification accuracy for multiclass microarray gene expression datasets.


Classification Accuracy Canonical Correlation Analysis Mutual Dependency Class Accuracy Feature Selection Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Dudoit, S., Fridly, J., Speed, T.P.: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data (June 2000),
  2. 2.
    Ooi, C.H., Chetty, M., Teng, S.W.: Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data. BMVC Journal 47, 1–19 (2006)Google Scholar
  3. 3.
    Tripathi, A., Klami, A., Kaski, S.: Simple integrative preprocessing preserves what is shared in data sources. BMC Bioinformatics 9, 111 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Bittner, M., et al.: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406(3), 536–540 (2000)CrossRefPubMedGoogle Scholar
  5. 5.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. Eighth Int’l Conf. Intelligent Systems for Molecular Biology (ISMB), vol. 8, pp. 93–103 (2000)Google Scholar
  6. 6.
    Duggan, D.J., Bittner, M.L., Chen, Y., Meltzer, P., Trent, J.M.: Expression profiling using cDNA microarrays. Nature Genetics 21, 10–14 (1999)CrossRefPubMedGoogle Scholar
  7. 7.
    Munagala, K., Tibshirani, R., Brown, P.: Cancer characterization and feature set extraction by discriminative margin clustering. BMC Bioinformatics 5, 21 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., et al.: Multi-class cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149–15154 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ross, D.T., Scherf, U., Eisen, M.B., Perou, C.M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S.S., Van de Rijn, M., Waltham, M., et al.: Systematic variation in gene expression patterns in human cance cell lines. Nat. Genet. 24, 227–235 (2000)CrossRefPubMedGoogle Scholar
  10. 10.
    Yeoh, E.-J., Ross, M.E., Shurtleff, S.A., Williams, W.K., Patel, D., Mahfouz, R., Behm, F.G., Raimondi, S.C., Relling, M.V., Patel, A., et al.: Classification, subtype discovery, and prediction of outcome in pediatric lymphoblastic leukemia by gene expression profiling. Cancer Cell 1(2), 133–143 (2002)CrossRefPubMedGoogle Scholar
  11. 11.
    Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., et al.: Classification and diagnostic prediction of cancers using expression profiling and artificial neural networks. Nat. Med. 7, 673–679 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Bhattacharjee, A., Richards, W.G., Staunton, J.E., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., et al.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 98, 13790–13795 (2001)CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Armstrong, S.A., Staunton, J.E., Silverman, L.B., Pieters, R., den Boer, M.L., Minden, M.D., Sallan, S.E., Lander, E.S., Golub, T.R., Korsmeyer, S.J.: MLL translocations specify adistinct gene expression profile that distinguishes a unique leukemia. Nat. Genet. 30, 41–47 (2002)CrossRefPubMedGoogle Scholar
  14. 14.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefPubMedGoogle Scholar
  15. 15.
    Borga, M.: Canonical correlation a tutorial (1999),
  16. 16.
    Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Girija Chetty
    • 1
  • Madhu Chetty
    • 2
  1. 1.Faculty of Information Sciences and EngineeringUniversity of CanberraAustralia
  2. 2.Faculty of Information TechnologyMonash UniversityVictoriaAustralia

Personalised recommendations