Hypotheses on a tree: new error rates and testing strategies
提出一种多重检验程序，能在树状层次结构中控制多个分辨率下的全局错误率，通过快速算法和模拟验证其有效性，并应用于基因表达调控和肠道微生物组与结直肠癌关系的研究。
Summary 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.