Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks

TL;DR

This paper introduces a covariate-adjusted statistical test for differential network analysis, improving accuracy in detecting true disease-related network differences by accounting for covariates, with applications in breast cancer gene networks.

Contribution

The paper proposes a novel covariate-adjusted test for differential network analysis that controls for covariate effects, enhancing detection accuracy over existing methods.

Findings

Improved type-I error control in simulations.

Enhanced power to detect differential connections.

Successful application to breast cancer gene networks.

Abstract

Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both the disease condition and the network. To address this issue, we propose a general covariate-adjusted test for differential network analysis. Our method assesses differential network connectivity by testing the null hypothesis that the network is the same for individuals who have identical covariates and only differ in disease status. We show empirically in a simulation study that the covariate-adjusted test exhibits improved type-I error control compared with na\"ive hypothesis testing procedures that do not account for covariates. We additionally show that there are settings in which our proposed methodology provides improved power to detect differential connections. We illustrate our method by applying it to detect differences in breast cancer gene co-expression networks by subtype.