Advertisement

Exploring Microarray Data with Correspondence Analysis

  • Stanislav Busygin
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 7)

Abstract

Due to the rapid development of DNA microarray chips it has become possible to discover and predict genetic patterns relevant for various diseases on the basis of exploration of massive data sets provided by DNA microarray probes. A number of data mining techniques have been used for such exploration to achieve the desirable results. However, high dimensionality and uncertain accuracy of microarray datasets remain the major obstacles in revealing the most crucial genetic factors determining a particular disease. This chapter describes a microarray data processing technique based on the correspondence analysis that helps to handle this issue.

Keywords

Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Singular Value Decomposition Correspondence Analysis Data Mining 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. Ben-Dor, L. Bruhn, I. Nachman, M. Schummer, and Z. Yakhini. Tissue classification with gene expression profiles. Journal of Computational Biology, 7:559–584, 2000.PubMedCrossRefGoogle Scholar
  2. 2.
    A. Ben-Dor, N. Friedman, and Z. Yakhini. Class discovery in gene expression data. Proceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB), 2001.Google Scholar
  3. 3.
    CAMDA 2001 Conference Contest Datasets. http://www.camda.duke.edu/camda01/datasets/.Google Scholar
  4. 4.
    P. J. Giles and D. Kipling. Normality of oligonucleotide microarray data and implications for parametric statistical analyses. Bioinformatics, 19:2254–2262, 2003.PubMedCrossRefGoogle Scholar
  5. 5.
    G. H. Golub and C. F. Van Loan. Matrix Computations, 3rd ed. (Johns Hopkins Series in the Mathematical Sciences). Baltimore, MD: John Hopkins, 1996.Google Scholar
  6. 6.
    T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999.PubMedCrossRefGoogle Scholar
  7. 7.
    M. J. Greenacre. Theory and Applications of Correspondence Analysis. Academic Press, 1984.Google Scholar
  8. 8.
    J. Kleinberg. The impossibility theorem for clustering. Proceedings of the NIPS 2002 Conference, 2002.Google Scholar
  9. 9.
    D. R. Masys, J. B. Welsh, J. L. Fink, M. Gribskov, I. Klacansky, and J. Corbeil. Use of keyword hierarchies to interpret gene expression patterns. Bioinformatics, 17:319–326, 2001.PubMedCrossRefGoogle Scholar
  10. 10.
    J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik. Feature selection for SVMs. Proceedings of the NIPS 2000 Conference, 2001.Google Scholar
  11. 11.
    E. P. Xing and R. M. Karp. CLIFF: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics Discovery Note, 1:1–9, 2001.Google Scholar
  12. 12.
    High-Density Array Pattern Interpreter (HAPI). http://array.ucsd.edu/hapi/Google Scholar
  13. 13.
    National Library of Medicine — MeSH. http://www.nlm.nih.gov/mesh/meshhome.htmlGoogle Scholar
  14. 14.
    Hierarchy of keywords from literature associated with the top 25 ALL genes reported by correspondence analysis. http://132.239.155.52/HAPI/ALL25_453.HTMLGoogle Scholar
  15. 15.
    Hierarchy of keywords from literature associated with the top 25 AML genes reported by correspondence analysis. http://132.239.155.52/HAPI/AML25_502.HTMLGoogle Scholar
  16. 16.
    Hierarchy of keywords from literature associated with the top 25 ALL genes reported by Golub at al. http://132.239.155.52/HAPI/goluball_911.HTMLGoogle Scholar
  17. 17.
    Hierarchy of keywords from literature associated with the top 25 AML genes reported by Golub at al. http://132.239.155.52/HAPI/golubaml_161.HTMLGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Stanislav Busygin
    • 1
  • Panos M. Pardalos
    • 1
  1. 1.Industrial and Systems Engineering DepartmentUniversity of FloridaGainesville

Personalised recommendations