论文标题
通过运行Ornstein-Uhlenbeck过程的超规模树对P值进行分层校正
Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process
论文作者
论文摘要
统计测试通常用作探索性工具，用于寻找表型与许多可能的解释变量之间的关联。这种方法通常会导致依赖性多次测试。我们假设通过树上的Ornstein-uhlenbeck过程进行测试之间的层次结构。过程相关结构用于平滑P值。我们设计了对p值计算的Ornstein-Uhlenbeck过程平均值的惩罚估计。通过模拟评估算法的性能。在宏基因组数据集中证明了它发现新关联的能力。相应的R软件包可从https://github.com/abichat/zazou获得。
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.
