A comparison study: applying segmentation to array CGH data for downstream analyses

DOI PDF 12 Citations
  • Hanni Willenbrock
    Center for Biological Sequence Analysis, Department of Biotechnology 1   1     Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
  • Jane Fridlyand
    Department of Epidemiology and Biostatistics, University of California at San Francisco 2   2     2340 Sutter Street, N224, San Francisco, CA 94143, USA

Description

<jats:title>Abstract</jats:title> <jats:p>Motivation: Array comparative genomic hybridization (CGH) allows detection and mapping of copy number of DNA segments. A challenge is to make inferences about the copy number structure of the genome. Several statistical methods have been proposed to determine genomic segments with different copy number levels. However, to date, no comprehensive comparison of various characteristics of these methods exists. Moreover, the segmentation results have not been utilized in downstream analyses.</jats:p> <jats:p>Results: We describe a comparison of three popular and publicly available methods for the analysis of array CGH data and we demonstrate how segmentation results may be utilized in the downstream analyses such as testing and classification, yielding higher power and prediction accuracy. Since the methods operate on individual chromosomes, we also propose a novel procedure for merging segments across the genome, which results in an interpretable set of copy number levels, and thus facilitate identification of copy number alterations in each genome.</jats:p> <jats:p>Availability:  </jats:p> <jats:p>Contact:  jfridlyand@cc.ucsf.edu</jats:p> <jats:p>Supplementary Information:  </jats:p>

Journal

  • Bioinformatics

    Bioinformatics 21 (22), 4084-4091, 2005-09-13

    Oxford University Press (OUP)

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