A Nonparametric Significance Test for Sampled Networks

TL;DR

This paper introduces a nonparametric Monte Carlo-based significance test to evaluate whether a subnetwork derived from a seed list significantly differs from random subnetworks, aiding in identifying meaningful subnetworks in protein interaction networks.

Contribution

It proposes a novel significance testing method that accounts for seed list redundancy and degree distribution, improving subnetwork selection in biological network analysis.

Findings

The method effectively distinguishes meaningful subnetworks from random ones.

It accounts for seed list redundancy and degree distribution in the null model.

The approach is applicable to protein-protein interaction networks.

Abstract

Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork, in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein-protein interaction network.