False Discovery Rate Control for High-Dimensional Networks of Quantile Associations Conditioning on Covariates



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats



Motivated by the gene co-expression pattern analysis, we propose a novel 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 squac to simultaneously test 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 numerical experiments that the squac testing method asymptotically controls the false discovery rate (fdr). It outperforms all alternative methods when the complex association panterns exist. Applied to a gastric cancer data, the squac method estimated the gene co-expression networks of early and late stage patients. It identified more changes in the networks which are associated with cancer survivals. We extend our method to the case that both the length of the outcomes and the length of covariates exceed the sample size, and show that the asymptotic theory still holds.







Xie, J, and Ruosha Li (2018). False Discovery Rate Control for High-Dimensional Networks of Quantile Associations Conditioning on Covariates. Retrieved from https://hdl.handle.net/10161/15996.



Jichun Xie

Associate Professor of Biostatistics & Bioinformatics

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.