Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing
Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing
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DOI：
10.1080/01621459.2020.1840992
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发表时间：
2019-10
影响因子：
3.7
通讯作者：
Luella Fu;Bowen Gang;Gareth M. James;Wenguang Sun
中科院分区：
文献类型：
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作者：
Luella Fu;Bowen Gang;Gareth M. James;Wenguang Sun
摘要标准化是多重检验中广泛采用的一种方法，因为它考虑了抽样的可变性，并使不同研究单元之间的检验统计量具有可比性。然而，尽管传统的智慧相反，我们表明，有可能是一个显着的信息损失的基础上标准化的统计假设检验，而不是完整的数据。我们开发了一类新的异方差调整的排名和阈值（哈特）的规则，旨在改善现有的方法，同时利用共性和调整异质性之间的研究单位。哈特的主要思想是通过直接将汇总统计量及其方差合并到测试程序中来绕过标准化。一个关键的信息是，替代分布的方差结构，这是归入标准化统计，是高度信息化，可以利用，以实现更高的权力。所提出的哈特程序被证明是渐近有效的，最佳的错误发现率（FDR）控制。我们的仿真结果表明，哈特实现了相当大的功率增益超过现有的方法在相同的FDR水平。我们通过骨髓瘤的微阵列分析来说明实施。
Abstract Standardization has been a widely adopted practice in multiple testing, for it takes into account the variability in sampling and makes the test statistics comparable across different study units. However, despite conventional wisdom to the contrary, we show that there can be a significant loss in information from basing hypothesis tests on standardized statistics rather than the full data. We develop a new class of heteroscedasticity-adjusted ranking and thresholding (HART) rules that aim to improve existing methods by simultaneously exploiting commonalities and adjusting heterogeneities among the study units. The main idea of HART is to bypass standardization by directly incorporating both the summary statistic and its variance into the testing procedure. A key message is that the variance structure of the alternative distribution, which is subsumed under standardized statistics, is highly informative and can be exploited to achieve higher power. The proposed HART procedure is shown to be asymptotically valid and optimal for false discovery rate (FDR) control. Our simulation results demonstrate that HART achieves substantial power gain over existing methods at the same FDR level. We illustrate the implementation through a microarray analysis of myeloma.