Dataset Information
Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.
Ontology highlight
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.
SUBMITTER: Guo B
PROVIDER: S-EPMC3023756 | biostudies-literature | 2010 Dec
REPOSITORIES: biostudies-literature
Publications
Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.
BMC research notes 20101230
<h4>Background</h4>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 inf ...[more]