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. 2015 Dec 22:3:e1525.
doi: 10.7717/peerj.1525. eCollection 2015.

Identifying communities from multiplex biological networks

Gilles Didier  1 , Christine Brun  2 , Anaïs Baudot  1
Affiliations

Identifying communities from multiplex biological networks

Gilles Didier et al. PeerJ. .

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.

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Conflict of interest statement

Anaïs Baudot is an Academic Editor for PeerJ. The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Adjusted Rand indexes between the reference community structure used to generate the random multiplex networks and the communities detected by the different approaches.
Multiplex networks contain from 1 to 9 graph layers. The indexes are averaged over 1,000 random multiplex networks of 1,000 vertices and 20 balanced communities. Sparse (resp. dense) multiplex networks are simulated with 0.1/0.01 (resp. 0.5/0.2) internal/external edge probability matrix. Mixed multiplex networks are simulated by uniformly sampling among these two matrices. Each vertex is withdrawn from each graph with a probability 0.5 to generate missing data.
Figure 2
Figure 2. Adjusted Rand Indexes between the partitions of the 4 individual networks and their sum-aggregation, union-aggregation, and the multiplex-modularity approach.
(A) Mean adjusted rand indexes for γ = 1 to γ = 15. (B) detailed adjusted rand indexes for γ = 5.
Figure 3
Figure 3. Percentage of communities associated to at least one significant GOBP term, for γ = 1 to γ = 15.
Figure 4
Figure 4. The 4 interaction layers of a module obtained after partitioning the multiplex biological networks with the multiplex-modularity approach.
From left to right: pathways, Co-expression, PPIs and Complexes networks. Proteins involved in the Coffin-Siris syndrome are highlighted in yellow, and protein related to other syndrome with shared clinical features are highlighted in blue.
See this image and copyright information in PMC

References

    1. Ahn Y-Y, Bagrow JP, Lehmann S. Link communities reveal multiscale complexity in networks. Nature. 2010;466(7307):761–764. doi: 10.1038/nature09182. - DOI - PubMed
    1. Aittokallio T, Schwikowski B. Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics. 2006;7(3):243–255. doi: 10.1093/bib/bbl022. - DOI - PubMed
    1. Arroyo R, Suñé G, Zanzoni A, Duran-Frigola M, Alcalde V, Stracker TH, Soler-López M, Aloy P. Systematic identification of molecular links between core and candidate genes in breast cancer. Journal of Molecular Biology. 2015;427(6):1436–1450. doi: 10.1016/j.jmb.2015.01.014. - DOI - PubMed
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nature Genetics. 2000;25(1):25–29. doi: 10.1038/75556. - DOI - PMC - PubMed
    1. Battiston F, Nicosia V, Latora V. Structural measures for multiplex networks. Physical Review E. 2014;89:032804. doi: 10.1103/PhysRevE.89.032804. - DOI - PubMed