Date of this Version
Journal of the American Statistical Association
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.
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
correlation, false discovery proportion, false discovery rate, multiple testing
Cai, T., & Liu, W. (2016). Large-Scale Multiple Testing of Correlations. Journal of the American Statistical Association, 111 (513), 229-240. http://dx.doi.org/10.1080/01621459.2014.999157
Date Posted: 25 October 2018
This document has been peer reviewed.