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

机译：贝叶斯-频率混合模型在基因拷贝数变化分析中的应用

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摘要

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.
机译：基因拷贝数（GCN）的变化是许多遗传疾病的共同特征。比较基因组杂交（CGH）是当今广泛使用的一种新技术，可用于筛选具有高分辨率全基因组的突变细胞中的GCN变化。用于分析此类CGH数据的统计方法正在发展。现有的方法是常客或完全贝叶斯方法。前者通常具有计算优势，而后者可以将先验信息合并到模型中，但是当一个人没有健全的先验信息时可能会产生误导。为了充分利用这两种方法的优势，我们开发了一种贝叶斯-频率混合方法，该方法通过贝叶斯方法来推断模型参数的子集，而其余参数则是通过贝叶斯方法来推断的。与单独使用的贝叶斯方法或常客方法相比，这种新的混合方法具有优势。当部分参数上有声音先验信息且样本量较小时，尤其如此。还讨论了空间依赖性和错误发现率，并且参数估计是有效的。作为说明，我们使用了建议的混合方法来分析实际的CGH数据。

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  • 来源
    《Journal of applied statistics》 |2011年第6期|p.987-1005|共19页
  • 作者

    Ao Yuan; Guanjie Chen; Juan Xiong; Wenqing He; Wen Jin; Charles Rotimi;

  • 作者单位

    National Human Genome Center, Howard University, Washington, DC, USA;

    Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, MD, USA;

    Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, Canada;

    Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, Canada;

    Suizhou Central Hospital, Suizhou, Hubei 441300, People's Republic of China;

    Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, MD, USA;

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  • 正文语种 eng
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  • 关键词

    bayesian; gene copy number; frequentist; hybrid model; prior information;

    机译：贝叶斯基因拷贝数;常客混合模型先验信息;

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