A nonparametric significance test for sampled networks

Elliott, A. , Leicht, E., Whitmore, A., Reinert, G. and Reed-Tsochas, F. (2018) A nonparametric significance test for sampled networks. Bioinformatics, 34(1), pp. 64-71. (doi: 10.1093/bioinformatics/btx419) (PMID:29036452) (PMCID:PMC5870844)

Abstract

Motivation: 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. Results: 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. Availability and implementation: The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Supplementary information: Supplementary data are available at Bioinformatics online.

• Item Type: Articles
• Additional Information: The authors acknowledge e-Therapeutics plc and the UK’s Engineering and Physical Sciences Research Council (EPSRC) for funding, via a studentship at the Systems Approaches to Biomedical Science Industrial Doctorate Centre at the University of Oxford. GR acknowledges support from EPSRC grant EP/K032402/1 and from the Oxford Martin School programme on Resource Stewardship. FRT acknowledges support from James Martin 21st Century Foundation grant LC1213-006.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elliott, Dr Andrew
• Authors: Elliott, A., Leicht, E., Whitmore, A., Reinert, G., and Reed-Tsochas, F.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1460-2059
• Published Online: 07 July 2017
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Bioinformatics 34(1): 64-71
• Publisher Policy: Reproduced under a Creative Commons License