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Biostatistics Advance Access published online on October 23, 2006
Biostatistics, doi:10.1093/biostatistics/kxl035
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received March 20, 2006
Revised September 1, 2006
Accepted October 16, 2006

Article

A hierarchical clustering method for estimating copy number variation

Baifang Xing 1, Celia M. T. Greenwood 2 *, and Shelley B. Bull 3

1 Genetics and Genomic Biology, Hospital for Sick Children, Toronto, ON, Canada and Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
2 Genetics and Genomic Biology, Hospital for Sick Children, Toronto, ON, Canada and Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada
3 Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, ON, Canada and Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada

* To whom correspondence should be addressed.
Celia M. T. Greenwood, E-mail: celia.greenwood{at}utoronto.ca


  Abstract

Microarray technologies allow for simultaneous measurement of DNA copy number at thousands of positions in a genome. Gains and losses of DNA sequences reveal themselves through characteristic patterns of hybridization intensity. To identify change points along the chromosomes we develop a marker clustering method which consists of two parts. First, a "Circular Clustering Tree Test Statistic" (CCTTS) attaches a statistic to each marker that measures the likelihood that it is a change point. Then construction of the marker statistics is followed by outlier detection approaches. The method provides a new way to build up a binary tree that can accurately capture change point signals and is easy to perform. A simulation study shows good performance in change point detection, and cancer cell line data are used to illustrate performance when regions of true copy number changes are known.


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