Posterior Probability of Discovery and Expected Rate of Discovery for Multiple Hypothesis Testing and High Throughput Assays
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多重检验与高通量检测中的发现后验概率和预期发现率
- 研究问题
- 在高通量测量产生大量检验、且精确零假设可能不符合实际时，如何用发现的可重复性衡量结果可信程度？
- 方法与思路
- 作者提出给定观测数据的发现后验概率PPD，并以假设子集的平均PPD定义预期发现率ERD，提供理论依据和计算算法。
- 材料陈述的贡献
- 摘要称这些指标可用于评价发现的可重复性及子集富集分析，并通过模拟和真实数据展示其有效性、用途与性能。
- 阅读关注点
- 应核对发现及重复实验的定义、后验模型和参数估计方式，并辨明PPD、ERD与错误发现率各自对应的统计目标。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明。摘要未说明后验模型条件或最优性的准则，不能将较高PPD视为无条件的重复成功保证。
原文摘要
Technologies of measuring millions of quantities at once have been rapidly developed and used in biological and biomedical research and other fields in the past decade. Yet a key issue remains unsettled, namely, how to control spurious findings for the ensuing massive number of hypothesis tests. An emerging consensus is to control false discovery rate (FDR), with FDR defined as the expected proportion of true nulls among discoveries, and a discovery the rejection of a null hypothesis. However, the very concept of counting true nulls, implicitly or explicitly, is problematic, for nulls are rarely true in reality. We propose an approach that is philosophically different from the FDR and other approaches. Taking advantage of the massive measurements, we can and should directly evaluate the reproducibility of a discovery by calculating the posterior probability of discovery (PPD) given observed data. A discovery with a great PPD is deemed to be highly reproducible, that is, not spurious. For a subset of hypotheses tested, mean PPD yields the expected rate of discovery (ERD), a measure useful for various applications such as subset enrichment analysis. We present here the rationale, theoretical basis, and an algorithm for computing PPDs and ERDs from data. Their validity, utility, and optimal performance are demonstrated using both simulated and real data. Supplementary material is available online.