Extracting Global Structure from Gene Expression Profiles

  • Charless Fowlkes
  • Qun Shan
  • Serge Belongie
  • Jitendra Malik

Abstract

We have developed a program, GENECUT, for analyzing datasets from gene expression profiling. GENECUT is based on a pairwise clustering method known as Normalized Cut [Shi and Malik, 1997]. GENECUT extracts global structures by progressively partitioning datasets into well-balanced groups, performing an intuitive k-way partitioning at each stage in contrast to commonly used 2-way partitioning schemes. By making use of the Nyström approximation, it is possible to perform clustering on very large genomic datasets.

Key words

gene expression profiles clustering analysis spectral partitioning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Buhmann, JM. Data Clustering and Learning. In: Arbib, MA, ed. The Handbook of Brain Theory and Neural Networks. MIT Press, 1995.Google Scholar
  2. Chung, FRK. Spectral Graph Theory. American Mathematical Society (1997).Google Scholar
  3. Duda, R, Hart, P. Pattern Classification and Scene Analysis. John Wiley & Sons (1973).Google Scholar
  4. Eisen, MB et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci 95 (1998): 14863–14868.PubMedCrossRefGoogle Scholar
  5. Fowlkes, C, Belongie, S, Malik, J. Spatiotemporal grouping using the Nyström approximation. Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn (2001).Google Scholar
  6. Hughes, TR, Marton, MJ et al. Functional discovery via a compendium of expression profiles. Cell 102 (2000): 109–126.PubMedCrossRefGoogle Scholar
  7. Ripley, BD. Pattern Recognition and Neural Networks. Cambridge (1996).Google Scholar
  8. Sharan, R, Shamir, R. Click: A clustering algorithm with applications to gene expression analysis. Proc. Of ISMB. AAAI Press, 2000.Google Scholar
  9. Shi, J, Malik, J. Normalized cuts and image segmentation. Proc IEEE Conf. Computer Vision and pattern Recognition (1997): 731–737.Google Scholar
  10. Shi, J, Malik, J. Normalized cuts and image segmentation. IEEE Trans. PAMI 22 (2000): 888–905.Google Scholar
  11. Tamayo, P et al. Interpreting patterns of gene expression with self-organizing maps: Methods and applications to hematopoietic differentiation. Proc. Natl. Acad. Sci. 96 (1999): 2907–2912.PubMedCrossRefGoogle Scholar
  12. Xing, EP, Karp, RM. Cliff: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Proc. Of the Nineteenth ISMB (2001).Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Charless Fowlkes
    • 1
  • Qun Shan
    • 2
  • Serge Belongie
    • 3
  • Jitendra Malik
    • 1
  1. 1.Departments of Computer ScienceUniversity of California at BerkeleyUSA
  2. 2.Molecular Cell BiologyUniversity of California at BerkeleyUSA
  3. 3.Department of Computer Science and EngineeringUniversity of California at San DiegoUSA

Personalised recommendations