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Bayesian--frequentist hybrid model with application to the analysis of gene copy number changes

Author

Listed:
  • Ao Yuan
  • Guanjie Chen
  • Juan Xiong
  • Wenqing He
  • Wen Jin
  • Charles Rotimi

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.

Suggested Citation

  • Ao Yuan & Guanjie Chen & Juan Xiong & Wenqing He & Wen Jin & Charles Rotimi, 2011. "Bayesian--frequentist hybrid model with application to the analysis of gene copy number changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 987-1005, February.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:987-1005
    DOI: 10.1080/02664761003692449
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    References listed on IDEAS

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    1. Guha, Subharup & Li, Yi & Neuberg, Donna, 2008. "Bayesian Hidden Markov Modeling of Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 485-497, June.
    2. Oscar M Rueda & Ramón Díaz-Uriarte, 2007. "Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-8, June.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
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    1. Le Chen & Ao Yuan & Aiyi Liu & Guanjie Chen, 2014. "Longitudinal data analysis using Bayesian-frequentist hybrid random effects model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2001-2010, September.

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