Statistics Papers

Document Type

Technical Report

Date of this Version

5-5-2016

Publication Source

Journal of the American Statistical Association

Volume

111

Issue

513

Start Page

229

Last Page

240

DOI

10.1080/01621459.2014.999157

Abstract

Multiple testing of correlations arises in many applications including gene coexpression network analysis and brain connectivity analysis. In this article, we consider large-scale simultaneous testing for correlations in both the one-sample and two-sample settings. New multiple testing procedures are proposed and a bootstrap method is introduced for estimating the proportion of the nulls falsely rejected among all the true nulls. We investigate the properties of the proposed procedures both theoretically and numerically. It is shown that the procedures asymptotically control the overall false discovery rate and false discovery proportion at the nominal level. Simulation results show that the methods perform well numerically in terms of both the size and power of the test and it significantly outperforms two alternative methods. The two-sample procedure is also illustrated by an analysis of a prostate cancer dataset for the detection of changes in coexpression patterns between gene expression levels. Supplementary materials for this article are available online.

Copyright/Permission Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of the American Statistical Association on 05 May 2016, available online: http://dx.doi.org/10.1080/01621459.2014.999157

Keywords

correlation, false discovery proportion, false discovery rate, multiple testing

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Date Posted: 25 October 2018

This document has been peer reviewed.