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Bioinformatics Advance Access originally published online on August 7, 2006
Bioinformatics 2006 22(23):2950-2951; doi:10.1093/bioinformatics/btl433
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

COPA—cancer outlier profile analysis

James W. MacDonald 1,* and Debashis Ghosh 2

1 University of Michigan Cancer Center, Ann Arbor MI, USA
2 Department of Biostatistics, University of Michigan Ann Arbor, MI, USA

*To whom correspondence should be addressed.

Summary: Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t-test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset.

Availability: http://www.bioconductor.org

Contact: jmacdon{at}med.umich.edu

Received on April 3, 2006; revised on August 3, 2006; accepted on August 3, 2006

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