Hypotheses on a tree: 新的错误率和测试策略。
Hypotheses on a tree: new error rates and testing strategies.
发表日期：2021 Sep
作者：
Marina Bogomolov, Christine B Peterson, Yoav Benjamini, Chiara Sabatti
来源：
BIOMETRIKA
摘要：
我们提出了一种多重检验程序，能够在多个分辨率级别上控制全局误差率。概念上，我们将这个问题框架化为在一棵树形结构中层次地选择假设。我们描述了一个快速算法，并证明在一定的p值依赖关系假设下，它能够控制相关误差率。通过模拟实验，我们证明了所提出的方法可以在多种依赖结构下提供所需的保证，并有可能比其他方法获得更高的功效。最后，我们将该方法应用于关于多种组织中基因表达的遗传调控和肠道微生物群与结直肠癌关系的研究中。
We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.