Format

Send to

Choose Destination
PeerJ. 2015 Dec 22;3:e1525. doi: 10.7717/peerj.1525. eCollection 2015.

Identifying communities from multiplex biological networks.

Author information

1
Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373 , Marseille , France.
2
Aix Marseille Université, Inserm, TAGC UMR_S1090 , Marseille , France ; CNRS , Marseille , France.

Abstract

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).

KEYWORDS:

Biological networks; Clustering; Coffin-Siris syndrome; Communities; Functional modules; Modularity; Multi-layer networks; Multiplex networks

Supplemental Content

Full text links

Icon for PeerJ, Inc. Icon for PubMed Central
Loading ...
Support Center