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

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...

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Bibliographic Details
Published in:Bioinformatics 2008-08, Vol.24 (16), p.1749-1756
Main Authors: Chen, Hung-I Harry, Hsu, Fang-Han, Jiang, Yuan, Tsai, Mong-Hsun, Yang, Pan-Chyr, Meltzer, Paul S., Chuang, Eric Y., Chen, Yidong
Format: Article
Language:English
Subjects:
Base Sequence
Biological and medical sciences
Chromosome Mapping - methods
Computer Simulation
DNA Probes - genetics
Fundamental and applied biological sciences. Psychology
Gene Dosage - genetics
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Molecular Sequence Data
Oligonucleotide Array Sequence Analysis - methods
Original Papers
Sequence Alignment - methods
Sequence Analysis, DNA - methods
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Summary: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@ntu.edu.tw Supplementary information: Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btn321