Introduction

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 RCNA 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 the 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.

Publications

  1. CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data.
    Cite this
    Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, Mardis ER, Wilson RK, Borecki IB, Province MA, 2010-02-01 - Bioinformatics (Oxford, England)

Credits

  1. Qunyuan Zhang
    Developer

    Division of Statistical Genomics, Washington University School of Medicine

  2. Li Ding
    Developer

  3. David E Larson
    Developer

  4. Daniel C Koboldt
    Developer

  5. Michael D McLellan
    Developer

  6. Ken Chen
    Developer

  7. Xiaoqi Shi
    Developer

  8. Aldi Kraja
    Developer

  9. Elaine R Mardis
    Developer

  10. Richard K Wilson
    Developer

  11. Ingrid B Borecki
    Developer

  12. Michael A Province
    Investigator

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Summary
AccessionBT005280
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByMichael A Province