Skip Navigation


Bioinformatics Advance Access originally published online on April 6, 2005
Bioinformatics 2005 21(12):2867-2874; doi:10.1093/bioinformatics/bti417
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/12/2867    most recent
bti417v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (8)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Levin, A. M.
Right arrow Articles by Kardia, S. L. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Levin, A. M.
Right arrow Articles by Kardia, S. L. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

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

1Department of Epidemiology, University of Michigan Ann Arbor, MI 48104-3028, USA
2Department of Biostatistics, University of Michigan Ann Arbor, MI 48109-2029, USA
3Department of Pathology, University of Michigan Ann Arbor, MI 48019-2216, USA

*To whom correspondence should be addressed at Department of Human Genetics, School of Medicine, University of Michigan, 1241 E. Catherine Street, Rm#5912, Ann Arbor, MI 48109-0618, USA.

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 10-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.

Contact: fcouples{at}umich.edu


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
A. Buness, R. Kuner, M. Ruschhaupt, A. Poustka, H. Sultmann, and A. Tresch
Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer
Bioinformatics, September 1, 2007; 23(17): 2273 - 2280.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. V. Sun, D. M. Jacobsen, and S. L. R. Kardia
ChromoScan: a scan statistic application for identifying chromosomal regions in genomic studies
Bioinformatics, December 1, 2006; 22(23): 2945 - 2947.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Callegaro, D. Basso, and S. Bicciato
A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions
Bioinformatics, November 1, 2006; 22(21): 2658 - 2666.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.