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Bioinformatics Advance Access published online on April 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti417
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received April 28, 2004
Revised March 24, 2005
Accepted March 29, 2005

Article

A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors

Albert M. Levin 1*, Debashis Ghosh 2, Kathleen R. Cho 3, and Sharon L. R. Kardia 1

1 Department of Epidemiology, University of Michigan, 611 Church St., Ann Arbor, MI 48104-3028
2 Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029
3 Department of Pathology, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI 48019-2216

* To whom correspondence should be addressed.
Albert M. Levin, E-mail: fcouples{at}umich.edu


  Abstract

Motivation: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density, and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression.

Results: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium to high level DNA amplification (DNA ratio values > 2). A classifier was developed from the scan statistic results that had a ten-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes.

Availability: Functions in the R-language are available from the author upon request.


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