Title: False Discovery Rate Control for High-Dimensional Networks of Quantile Associations Conditioning on Covariates
时间: 5月19日下3:00-4:00
地点：明德主楼1030
报告人：Jichun Xie
Assistant Professor
Department of Biostatistics and Bioinformatics
Duke University
Abstract:
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 permutation, 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.
Short bio:
Dr. Xie got her PhD degree in Biostatistics from University of Pennsylvania in 2011 and had been working at Temple University Fox School of Business for three years. She joined the Department of Biostatistics & Bioinformatics at Duke as an Assistant Professor in July 2014. She is actively involved in the research activities across the Duke Center of Statistical Genetics and Genomics, Duke Cancer Institute, and Duke Department of Neurosurgery. Her research focuses on identifying rare events in complex mixtures, inferring high-dimensional general dependence networks, and their applications in translational biomedical research.