Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;38(5):987-1005.
doi: 10.1080/02664761003692449.

Bayesian Frequentist Hybrid Model Wth Application to the Analysis of Gene Copy Number Changes

Affiliations
Free PMC article

Bayesian Frequentist Hybrid Model Wth Application to the Analysis of Gene Copy Number Changes

Ao Yuan et al. J Appl Stat. .
Free PMC article

Abstract

Gene copy number (GCN) changes are common characteristics of many genetic diseases. Comparative genomic hybridization (CGH) is a new technology widely used today to screen the GCN changes in mutant cells with high resolution genome-wide. Statistical methods for analyzing such CGH data have been evolving. Existing methods are either frequentist's, or full Bayesian. The former often has computational advantage, while the latter can incorporate prior information into the model, but could be misleading when one does not have sound prior information. In an attempt to take full advantages of both approaches, we develop a Bayesian-frequentist hybrid approach, in which a subset of the model parameters is inferred by the Bayesian method, while the rest parameters by the frequentist's. This new hybrid approach provides advantages over those of the Bayesian or frequentist's method used alone. This is especially the case when sound prior information is available on part of the parameters, and the sample size is relatively small. Spatial dependence and false discovery rate are also discussed, and the parameter estimation is efficient. As an illustration, we used the proposed hybrid approach to analyze a real CGH data.

Keywords: Bayesian; Frequentist; Gene copy number; Hybrid model; prior information.

Figures

Figure 1
Figure 1
Classification results from independence model:1. GCN deletion; 2. Normal; 3. Amplification.
Figure 2
Figure 2
Classification results from dependence model:1. GCN deletion; 2. Normal; 3. Amplification.

Similar articles

See all similar articles

Cited by 1 article

LinkOut - more resources

Feedback