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Bioinformatics Advance Access published online on August 30, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti646
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received June 2, 2005
Revised August 18, 2005
Accepted August 24, 2005

Article

Detection of DNA copy number alterations using penalized least squares regression

Tao Huang 1, Baolin Wu 2, Paul Lizardi 3, and Hongyu Zhao 4*

1 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA
2 Division of Biostatistics, School of Public Health, University of Minnesota A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA
3 Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Molecular Biochemistry and Biophysics, Yale University School of Medicine, New Haven, CT 06520, USA
4 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA

* To whom correspondence should be addressed.
Hongyu Zhao, E-mail: hongyu.zhao{at}yale.edu


  Abstract

Motivation: Genomic DNA copy number alterations are characteristic of many human diseases including cancer. Various techniques and platforms have been proposed to allow researchers to partition the whole genome into segments where copy numbers change between contiguous segments, and subsequently to quantify DNA copy number alterations. In this paper, we incorporate the spatial dependence of DNA copy number data into a regression model and formalize the detection of DNA copy number alterations as a penalized least squares regression problem. In addition, we use a stationary bootstrap approach to estimate the statistical significance and false discovery rate.

Results: The proposed method are studied by simulations and illustrated by an application to an extensively analyzed data set in the literature. The results show that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives.

Availability: http://bioinformatics.med.yale.edu/DNACopyNumber.


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