Hypotheses on a tree: new error rates and testing strategies.
Hypotheses on a tree: new error rates and testing strategies.
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树上的假设：新的错误率和测试策略。
DOI：
10.1093/biomet/asaa086
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发表时间：
2021
期刊：
影响因子：
2.7
通讯作者：
Sabatti,Chiara
中科院分区：
文献类型：
--
作者：
Bogomolov,Marina;Peterson,ChristineB;Benjamini,Yoav;Sabatti,Chiara
我们引入了一个多重测试程序，控制在多个级别的分辨率的全局错误率。从概念上讲，我们将这个问题定义为在树结构中分层组织的假设的选择。我们描述了一个快速算法，并证明了它控制相关的错误率给定一定的假设值之间的依赖关系。通过模拟，我们表明，所提出的程序提供了所需的保证下的依赖结构的范围内，它有可能获得替代方法的权力。最后，我们将该方法应用于研究多个组织中基因表达的遗传调控以及肠道微生物组与结直肠癌之间的关系。
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-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.