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Discovering classes in microarray data using island counts

Author

Listed:
  • Brendan Mumey

    (Montana State University)

  • Louise Showe

    (The Wistar Institute)

  • Michael Showe

    (The Wistar Institute)

Abstract

We present a new biclustering algorithm to simultaneously discover tissue classes and identify a set of genes that well-characterize these classes from DNA microarray data sets. We employ a combinatorial optimization approach where the object is to simultaneously identify an interesting set of genes and a partition of the array samples that optimizes a certain score based on a novel color island statistic. While this optimization problem is NP-complete in general, we are effectively able to solve problems of interest to optimality using a branch-and-bound algorithm. We have tested the algorithm on a 30 sample Cutaneous T-cell Lymphoma data set; it was able to almost perfectly discriminate short-term survivors from long-term survivors and normal controls. Another useful feature of our method is that can easily handle missing expression data.

Suggested Citation

  • Brendan Mumey & Louise Showe & Michael Showe, 2007. "Discovering classes in microarray data using island counts," Journal of Combinatorial Optimization, Springer, vol. 13(3), pages 207-216, April.
    • Handle: RePEc:spr:jcomop:v:13:y:2007:i:3:d:10.1007_s10878-006-9021-9
    DOI: 10.1007/s10878-006-9021-9
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