Differential analysis of biological networks

TL;DR

This paper introduces the dGHD algorithm, a scalable statistical method for detecting localized topological differences in biological networks, aiding in cancer biomarker discovery.

Contribution

The paper presents the dGHD algorithm, which uses the Generalised Hamming Distance and asymptotic normal approximation for efficient two-network comparison.

Findings

dGHD outperforms standard Hamming distance in detecting subtle differences

High sensitivity and specificity demonstrated in simulation studies

Application to ovarian cancer networks identifies potential biomarkers

Abstract

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.