通过伽马分布合并从属 P 值。

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Li-Chu Chien
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引用次数: 0

摘要

结合多个假设检验的相关 p 值是遗传和基因组数据分析中最常用的信息整合方法。然而,大多数现有的将来自单个成分问题的独立 p 值合并为一个统一 p 值的方法都不适合多重假设检验中 p 值之间的相关结构。虽然对现有的一些 p 值组合方法进行了修改,以克服潜在的局限性,但在遗传数据分析中,还没有统一的最强大的相关 p 值组合方法。因此,有必要提供一种既能稳健地控制 I 型误差又能保持良好幂率的 p 值组合方法。在本文中,我们提出了一种基于伽马分布(EMGD)的经验方法,用于组合多重假设检验的相关 p 值。所提出的 EMGD 检验可以灵活地将多重假设检验中高度相关的 p 值合并成一个统一的 p 值,用于检验我们感兴趣的组合假设。EMGD 保留了经验布朗法(EBM)的稳健性,可用于汇集多重假设检验的依存 p 值。此外,EMGD 还保留了基于伽马分布的方法的特点,即同时保留了 z 变换检验和伽马变换检验的优点,用于合并来自多个统计检验的从属 p 值。这两种特性使得 EMGD 在组合多重假设检验的因变量 p 值时能保持稳健的功率。通过与 Kost 和 McDermott 方法、EBM 和调和均值 p 值方法等现有方法的比较,模拟和实际数据应用说明了所提出的 EMGD 方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining dependent p-values by gamma distributions.

Combining correlated p-values from multiple hypothesis testing is a most frequently used method for integrating information in genetic and genomic data analysis. However, most existing methods for combining independent p-values from individual component problems into a single unified p-value are unsuitable for the correlational structure among p-values from multiple hypothesis testing. Although some existing p-value combination methods had been modified to overcome the potential limitations, there is no uniformly most powerful method for combining correlated p-values in genetic data analysis. Therefore, providing a p-value combination method that can robustly control type I errors and keep the good power rates is necessary. In this paper, we propose an empirical method based on the gamma distribution (EMGD) for combining dependent p-values from multiple hypothesis testing. The proposed test, EMGD, allows for flexible accommodating the highly correlated p-values from the multiple hypothesis testing into a unified p-value for examining the combined hypothesis that we are interested in. The EMGD retains the robustness character of the empirical Brown's method (EBM) for pooling the dependent p-values from multiple hypothesis testing. Moreover, the EMGD keeps the character of the method based on the gamma distribution that simultaneously retains the advantages of the z-transform test and the gamma-transform test for combining dependent p-values from multiple statistical tests. The two characters lead to the EMGD that can keep the robust power for combining dependent p-values from multiple hypothesis testing. The performance of the proposed method EMGD is illustrated with simulations and real data applications by comparing with the existing methods, such as Kost and McDermott's method, the EBM and the harmonic mean p-value method.

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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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