A nonparametric significance test for sampled networks

Abstract

MotivationOur 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.ResultsWe 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.Availability and implementationThe software used in this paper are available for download at . The software is written in Python and uses the NetworkX library. Supplementary information Supplementary data are available at Bioinformatics online.

Galectin-3 Regulates γ-Herpesvirus Specific CD8 T Cell Immunity

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“…The average local clustering coefficient was found to be 0.721. A value of clustering coefficient nearing 0 suggests no clustering, whereas a value of 1 represents maximal clustering ( Elliott et al., 2018 ). Many of the genes present in the network have been studied during differentiation of T cells expanded during infectious agents ( Best et al., 2013 , Wherry and Ahmed, 2004 , Wherry et al., 2007 ).…”

Physiological change alters endophytic bacterial community in clubroot of tumorous stem mustard infected by Plasmodiophora brassicae

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Infection of Plasmodiophora brassicae changes the fungal endophyte community of tumourous stem mustard roots as revealed by high-throughput sequencing and culture-dependent methods

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