Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.

TitleBayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.
Publication TypeJournal Article
Year of Publication2010
AuthorsGuo, B, Villagran, A, Vannucci, M, Wang, J, Davis, C, Man, T-K, Lau, C, Guerra, R
JournalBMC Res Notes
Volume3
Pagination350
Date Published2010 Dec 30
ISSN1756-0500
Abstract

BACKGROUND: The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.RESULTS: The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.CONCLUSIONS: We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.

DOI10.1186/1756-0500-3-350
Alternate JournalBMC Res Notes
PubMed ID21192799
PubMed Central IDPMC3023756