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Bioinformatics Advance Access originally published online on December 6, 2006
Bioinformatics 2007 23(4):450-457; doi:10.1093/bioinformatics/btl624
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Markers improve clustering of CGH data

Jun Liu *, Sanjay Ranka and Tamer Kahveci

Computer and Information Science and Engineering, University of Florida Gainesville, FL 32611, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples using similarity based clustering methods. A key requirement for clustering is to avoid using the noisy aberrations in the CGH samples.

Results: We develop a dynamic programming algorithm to identify a small set of important genomic intervals called markers. The advantage of using these markers is that the potentially noisy genomic intervals are excluded during the clustering process. We also develop two clustering strategies using these markers. The first one, prototype-based approach, maximizes the support for the markers. The second one, similarity-based approach, develops a new similarity measure called RSim and refines clusters with the aim of maximizing the RSim measure between the samples in the same cluster. Our results demonstrate that the markers we found represent the aberration patterns of cancer types well and they improve the quality of clustering significantly.

Availability: All software developed in this paper and all the datasets used are available from the authors upon request.

Contact: juliu{at}cise.ufl.edu

Associate Editor: John Quackenbush

Received on September 24, 2006; revised on December 4, 2006; accepted on December 4, 2006

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