Abstract
The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challengeestablished a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challengebenchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.
Highlights
Biological macromolecules do not act isolated in cells, but interact with each other to perform their functions, in signaling or metabolic pathways, molecular complexes, or, more generally, biological processes
The clustering approaches proposed by the participants are assessed regarding their capacity to reveal disease communities, defined as communities significantly associated with genes implicated in diseases in GWAS studies[3,4]
MolTi was designed for multiplex networks, it deals with monoplex networks by considering them as multiplexes composed of a single layer
Summary
Biological macromolecules do not act isolated in cells, but interact with each other to perform their functions, in signaling or metabolic pathways, molecular complexes, or, more generally, biological processes. A common feature of biological networks is their modularity, i.e., their organization around communities - or functional modules - of tightly connected genes/proteins implicated in the same biological processes[1,2]. The Disease Module Identification (DMI) DREAM challenge aims at investigating different algorithms dedicated to the identification of communities, in a biomedical context[3]. The challengers proposed various strategies and clustering approaches, including kernel clustering, random walks or modularity optimization. MolTi was initially developed to cluster multiplex networks, i.e., networks composed of different layers of interactions. We have demonstrated that this multiplex approach better identifies the communities than approaches merging the networks, or performing consensus clusterings, both on simulated and real biological datasets[5]
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