Inference for High-dimensional Differential Correlation Matrices

TL;DR

This paper develops an adaptive thresholding method for estimating and testing high-dimensional differential correlation matrices, with theoretical guarantees and applications in genomics, outperforming existing methods.

Contribution

It introduces a novel adaptive thresholding estimator with proven minimax optimal convergence rates for differential correlation matrices.

Findings

Estimator outperforms existing methods in simulations

Method effectively detects gene co-expression differences in breast cancer data

Provides a new hypothesis testing approach for sparse differential correlation matrices

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.