Parametric Change-point Models for the Identification of DNA

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TITLE: Parametric Change-point Models for the Identification of DNA Copy Number Variations

SPEAKER: Dr. Jie Chen


To study the genetic reasons of tumor growth, cancer formation, and genetic diseases, researchers can now use advanced bio-technologies to conduct DNA copy number experiments as it is believed that cancer development and genetic diseases are usually associated with DNA copy number changes in the genome. One of such technologies commonly used in DNA copy number experiments is called the array comparative genomic hybridization (aCGH), and the resulting data is called aCGH data. In aCGH data, gene positions, log base 2 ratio intensities along the biomarkers, and other genomic features, etc., are acquired. One of the important goals of analyzing the aCGH data is to indentify the loci at which there are abberrations of DNA copy numbers.

It turns out that identifying loci of DNA copy number changes or variations along the chromosome or genome can be viewed as a change point problem of detecting the changes presented in the sequence of log ratio intensities. In this talk, I will introduce the mean and variance change point model that is suitable for analyzing the aCGH data. Two approaches, namely the information criterion approach and a Bayesian approach, will be used to analyze the aCGH data in the proposed change-point model setting. Several applications of the proposed change point methods to the analysis of fibroblast cell line aCGH data, the breast tumor aCGH data, and the breast cancer aCGH data will be given.


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:10/12/2009
  • Modified By:Fletcher Moore
  • Modified:10/07/2016