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
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]
Randomization improves community detection on simulated multiplex networks To evaluate the accuracy of the community structures detected from the initial MolTi and its improved version that includes the randomization procedure, we simulated random multiplex networks with a fixed, known community structure, and various features (Methods)
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. Thanks to the development of experimental techniques and to the extraction of knowledge accumulated in the literature, biological networks are nowadays assembled on a large scale. 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]
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