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BMC Research Notes

, 3:350

First Online: 30 December 2010Received: 29 March 2010Accepted: 30 December 2010DOI: 10.1186-1756-0500-3-350

Cite this article as: Guo, B., Villagran, A., Vannucci, M. et al. BMC Res Notes 2010 3: 350. doi:10.1186-1756-0500-3-350

Abstract

BackgroundThe 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.

ResultsThe 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.

ConclusionsWe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1756-0500-3-350 contains supplementary material, which is available to authorized users.

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Autor: Beibei Guo - Alejandro Villagran - Marina Vannucci - Jian Wang - Caleb Davis - Tsz-Kwong Man - Ching Lau - Rudy Guerra

Fuente: https://link.springer.com/







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