使用自适应滤波过程检测多个复制信号
Detecting Multiple Replicating Signals using Adaptive Filtering Procedures
摘要：
可复制性是科学发现的一个基本特征：我们感兴趣的是那些在不同实验室、研究人群、不同时间等中可检测到的信号。与元分析不同，元分析解释了实验变异性，但不保证可复制性，测试部分连接（PC）null的目的是专门识别在多项研究中发现的信号。在许多当代应用中，例如，为了比较多个高通量遗传实验，需要同时测试大量的$M$ PC空值，这需要多重比较校正。但是，对$M$ PC $p$-值进行标准多次测试调整可能非常保守，尤其是当$M$较大且信号稀疏时。我们介绍AdaFilter，一个新的多重检验程序，通过自适应地过滤掉不太可能的候选PC空值来增加功率。我们证明了AdaFilter可以控制FWER和FDR，只要研究之间的数据是独立的，并具有更高的权力比其他现有的方法。我们用三个例子说明AdaFilter的应用：杜氏肌营养不良症的微阵列研究，肺癌肿瘤中T细胞的单细胞RNA测序和GWAS的代谢组学。
英文摘要：
Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, study populations, across time etc. Unlike meta-analysis which accounts for experimental variability but does not guarantee replicability, testing a partial conjunction (PC) null aims specifically to identify the signals that are discovered in multiple studies. In many contemporary applications, e.g., comparing multiple high-throughput genetic experiments, a large number $M$ of PC nulls need to be tested simultaneously, calling for a multiple comparisons correction. However, standard multiple testing adjustments on the $M$ PC $p$-values can be severely conservative, especially when $M$ is large and the signals are sparse. We introduce AdaFilter, a new multiple testing procedure that increases power by adaptively filtering out unlikely candidates of PC nulls. We prove that AdaFilter can control FWER and FDR as long as data across studies are independent, and has much higher power than other existing methods. We illustrate the application of AdaFilter with three examples: microarray studies of Duchenne muscular dystrophy, single-cell RNA sequencing of T cells in lung cancer tumors and GWAS for metabolomics.
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