Identifying communities from multiplex biological networks.
Résumé
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).