Refining modules to determine functionally significant clusters in molecular networks

Jagath C. Rajapakse,Rama Kaalia

doi:10.1186/s12864-019-6294-9

Abstract

BackgroundModule detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks.ResultsThe module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks.ConclusionThe proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

Highlights

Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities
We propose a new module refinement algorithm that refines the modules obtained from any modularity based community detection method
First set of experiments were performed on benchmark synthetic networks and real human protein-protein interaction networks (PPIN)

Summary

Introduction

Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. Many algorithms have been introduced to obtain biologically significant modules of genes, proteins and metabolites [5,6,7,8,9,10,11,12,13] These algorithms use topological properties of networks to cluster nodes into modules; a module being defined as a subgraph of nodes, having more dense connections among themselves than with the rest of the network. Modularity based module detection is based on rearranging nodes in modules to maximize the modularity of the resulting partitioning [5, 6, 9, 22] These algorithms have shown good performances in many biological applications [23] but suffer from a resolution limit as they fail to detect small modules [24]. The other global quality functions mathematically similar to modularity, where the quality of a Kaalia and Rajapakse BMC Genomics _#####################_

Methods

Results

Discussion

Conclusion