CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data

Item Type Journal Article
Author Qunyuan Zhang
Author Li Ding
Author David E Larson
Author Daniel C Koboldt
Author Michael D McLellan
Author Ken Chen
Author Xiaoqi Shi
Author Aldi Kraja
Author Elaine R Mardis
Author Richard K Wilson
Author Ingrid B Boreki
Author Michael A Province
URL http://www.ncbi.nlm.nih.gov/pubmed/20031968
Publication Bioinformatics (Oxford, England)
ISSN 1367-4811
Date Dec 23, 2009
Extra PMID: 20031968
Journal Abbr Bioinformatics
DOI 10.1093/bioinformatics/btp708
Accessed 2010-01-01 14:14:14
Library Catalog NCBI PubMed
Abstract MOTIVATION: DNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly-used methods for RNCA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden as well as a loss of the overall statistical power due to segmentation and discretization of individual sample's data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient, and particularly suitable for high-resolution and large-population studies. RESULTS: Our approach, Correlation Matrix Diagonal Segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS, and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes. AVAILABILITY: The R and C programs implementing our method are available at https://dsgweb.wustl.edu/qunyuan/software/cmds. CONTACT: qunyuan@wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Title CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data
Short Title CMDS
Date Added 2009-01-01 09:14
Date Modified 2009-01-01 09:14