Statistics Papers

Document Type

Technical Report

Date of this Version

1-2016

Publication Source

Journal of Multivariate Analysis

Volume

143

Start Page

107

Last Page

126

DOI

10.1016/j.jmva.2015.08.019

Abstract

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed.

Copyright/Permission Statement

© 2016 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

adaptive thresholding, covariance matrix, differential co-expression analysis, differential correlation matrix, optimal rate of convergence, sparse correlation matrix, thresholding

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Date Posted: 25 October 2018

This document has been peer reviewed.