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Bioinformatics Advance Access published online on February 2, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl035
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received December 7, 2005
Revised January 28, 2006
Accepted January 31, 2006

Article

Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model

Philippe Broët 1 * and Sylvia Richardson 2

1 Faculté de Médecine - Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France
2 Department of Epidemiology and Public Health, Imperial College, Norfolk Place, London W2 1PG, United Kingdom

* To whom correspondence should be addressed.
Philippe Broët, E-mail: broet{at}vjf.inserm.fr


  Abstract

Motivation: Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them.

Results: Using simulated data, CGHmix is validated and compared to both a conventional unstructured mixture model and to a recently proposed data mining method. We demonstrate the good performance of CGHmix for classifying copy number changes. Additionally, the method provides a good estimate of the False Discovery Rate. We also present the analysis of a cancer related dataset.

Supplementary information: http://www.bgx.org.uk/papers.html.

Associate Editor: Martin Bishop
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