title: CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data.
abstract: BACKGROUND
  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.
authors.affiliation: Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu
authors.forename: Qunyuan
authors.initials: Q
authors.lastname: Zhang
authors.forename: Li
authors.initials: L
authors.lastname: Ding
authors.forename: David E
authors.initials: DE
authors.lastname: Larson
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Koboldt
authors.forename: Michael D
authors.initials: MD
authors.lastname: McLellan
authors.forename: Ken
authors.initials: K
authors.lastname: Chen
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Shi
authors.forename: Aldi
authors.initials: A
authors.lastname: Kraja
authors.forename: Elaine R
authors.initials: ER
authors.lastname: Mardis
authors.forename: Richard K
authors.initials: RK
authors.lastname: Wilson
authors.forename: Ingrid B
authors.initials: IB
authors.lastname: Borecki
authors.forename: Michael A
authors.initials: MA
authors.lastname: Province
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics*; D030541:Databases, Genetic; D018628:Gene Dosage / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D005828:Genetics, Population; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
chemical_list: D004247:DNA