Statistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.

统计测试通常用作探索性工具来搜索表型和许多可能的解释变量之间的关联。这种方法通常会导致在依赖下进行多次测试。我们通过树上的 Ornstein-Uhlenbeck 过程假设测试之间的层次结构。过程相关结构用于平滑p 值。我们设计了一个用于p值计算的 Ornstein-Uhlenbeck 过程均值的惩罚估计。该算法的性能通过模拟进行评估。它发现新关联的能力在宏基因组数据集上得到了证明。相应的 R 包可从 https://github.com/abichat/zazou 获得。