title: A hierarchical clustering method for estimating copy number variation.
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 2 parts. First, a "circular clustering tree test statistic" 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.
authors.affiliation: Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada.
authors.forename: Baifang
authors.initials: B
authors.lastname: Xing
authors.forename: Celia M T
authors.initials: CM
authors.lastname: Greenwood
authors.forename: Shelley B
authors.initials: SB
authors.lastname: Bull
mesh_terms: D000465:Algorithms; D001699:Biometry; D002460:Cell Line; D002877:Chromosomes, Human / Q000235:genetics; D016000:Cluster Analysis; D004247:DNA / Q000235:genetics; D018628:Gene Dosage; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data
chemical_list: D004247:DNA