False discovery rate control for high dimensional networks of quantile associations conditioning on covariates.

ABSTRACT: Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously the independence between one pair of variables conditioning on covariates for all p(p-1)/2 pairs. Here, p is the length of the outcomes and could exceed the sample size. The testing procedure does not require resampling or perturbation and thus is computationally efficient. We prove by theory and num

SUBMITTER: Xie J 

PROVIDER: S-EPMC6497089 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature