Bioinformatics Advance Access originally published online on May 16, 2006
Bioinformatics 2006 22(16):1971-1978; doi:10.1093/bioinformatics/btl185
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Distance-based clustering of CGH data
1 Computer and Information Science and Engineering, University of Florida Gainesville FL, 32611 USA
2 Institut fuer Humangenetik, Rheinisch-Westfaelische Technische Hochschule Aachen, Germany
*To whom correspondence should be addressed.
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Abstract |
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Motivation: We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar.
Results: We focus on distance-based clustering strategies. We do this in two steps. (1) Distances of all pairs of CGH samples are computed. (2) CGH samples are clustered based on this distance. We develop three pairwise distance/similarity measures, namely raw, cosine and sim. Raw measure disregards correlation between contiguous genomic intervals. It compares the aberrations in each genomic interval separately. The remaining measures assume that consecutive genomic intervals may be correlated. Cosine maps pairs of CGH samples into vectors in a high-dimensional space and measures the angle between them. Sim measures the number of independent common aberrations. We test our distance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means with and without centroid shrinking. Our results show that sim consistently performs better than the remaining measures. This indicates that the correlation of neighboring genomic intervals should be considered in the structural analysis of CGH datasets. The combination of sim with top-down clustering emerged as the best approach.
Availability: All software developed in this article and all the datasets are available from the authors upon request.
Contact: juliu{at}cise.ufl.edu
This material is based upon work supported by the National Science Foundation under Grant ITR 0325459. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Associate Editor: Martin Bishop
Received on February 6, 2006; revised on April 20, 2006; accepted on May 10, 2006
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