Publication

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

Bioinformatics. 2009 Dec 23. [Epub ahead of print]

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.

Authors

Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, Mardis ER, Wilson RK, Boreki IB, Province MA.