Detection of DNA copy number changes using statistical change point analysis

Jie Chen, Yu Ping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Cancer development is usually associated with DNA copy number changes in the genome. DNA copy number changes correspond to chromosomal aberrations and signify abnormality of a cell. Therefore, identifying statistically significant DNA copy number changes is evidently crucial in cancer research, clinical diagnostic applications, and other related genomic research. The problem can be formulated with a statistical change point theory. We propose to use the mean and variance change point model to study the DNA copy number changes from the microarray comparative genomic hybridization (aCGH) profile. The approximate p-value of identifying a change point is derived from the use of Schwarz information criterion (SIC). The proposed method has been validated by Monte-Carlo simulation and applications to aCGH profiles from several cell lines (fibroblast cancer cell line, breast tumor cell line, and breast cancer cell line). The results indicate that the proposed method is effective in identifying DNA copy number changes.

Original languageEnglish (US)
Title of host publication2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006
Pages11-12
Number of pages2
DOIs
StatePublished - Dec 1 2006
Externally publishedYes
Event2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 - College Station, TX, United States
Duration: May 28 2006May 30 2006

Publication series

Name2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006

Other

Other2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
Country/TerritoryUnited States
CityCollege Station, TX
Period5/28/065/30/06

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Statistics and Probability

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