Didier Gilles, Brun Christine, Baudot Anaïs
Aix Marseille Université, CNRS, Centrale Marseille, I2M UMR 7373 , Marseille , France.
Aix Marseille Université, Inserm, TAGC UMR_S1090 , Marseille , France ; CNRS , Marseille , France.
PeerJ. 2015 Dec 22;3:e1525. doi: 10.7717/peerj.1525. eCollection 2015.
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).
可以构建各种生物网络，每个网络都具有不同含义的基因/蛋白质关系（例如，蛋白质相互作用或基因共表达）。然而，经典地并未考虑这种多样性，并且不同的相互作用类别通常被聚集在单个网络中。多重框架中，生物关系由反映相互作用各种性质的不同网络层表示，有望保留更多信息。在这里，我们评估了从多个网络源检测群落的聚集、共识和多重模块性方法。通过模拟随机网络，我们证明当网络层不完整或密度不均匀时，多重模块性方法优于聚集和共识方法。应用于包含4层物理或功能相互作用的多重生物网络时，与聚集后的对应网络相比，能够更准确地恢复注释的群落。总体而言，考虑生物网络的多重性会导致功能模块定义得更好。一个用于从多重网络检测群落的用户友好型图形软件以及相应的C源代码可在GitHub（https://github.com/gilles-didier/MolTi）上获取。
