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Bioinformatics Advance Access originally published online on July 4, 2008
Bioinformatics 2008 24(16):1749-1756; doi:10.1093/bioinformatics/btn321
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A probe-density-based analysis method for array CGH data: simulation, normalization and centralization

Hung-I Harry Chen 1,2, Fang-Han Hsu 1,2, Yuan Jiang 3, Mong-Hsun Tsai 2,4, Pan-Chyr Yang 5, Paul S. Meltzer 3, Eric Y. Chuang 1,2,4,6,7,8,* and Yidong Chen 3

1Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, 2Research Center for Medical Excellence, National Taiwan University, Taipei, Taiwan 100, Republic of China, 3Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA, 4Institute of Biotechnology, Center for Systems Biology and Bioinformatics, National Taiwan University, Taipei, Taiwan 106, 5College of Medicine, National Taiwan University, Taipei, Taiwan 100, 6Department of Life Science, National Taiwan University, Taipei, Taiwan 106, 7Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan 106 and 8Graduate Institute of Epidemiology, National Taiwan University, Taipei, Taiwan 106, Republic of China

*To whom correspondence should be addressed.


  Abstract

Motivation: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results.

Results: We demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines.

Contact: chuangey{at}ntu.edu.tw

Supplementary information: Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary

Associate Editor: John Quackenbush

Received on January 25, 2008; revised on May 28, 2008; accepted on June 18, 2008

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