identification of communities from multiplex networks

Multiplex Biological NetworksCommunities

1

BioNetVisa WS25/11/2015Singaporeanais.baudot@univ-amu.fr

I2M

Systems Biology

Genotypes

Phenotypes

-OmicsNetworks

Large-scale Protein-protein Interaction Networks

Interactomes

How to extract relevant biological information ?

Identification of communitiesClasses / Clusters / Modules

Protein cellular functions

Group of tightly linked genes/proteins

6

Inspired from P. Aloy, ECCB 2014

Protein - Structures Functional modules Interaction Networks

ComprehensivenessPrecision

• Topology : Density, Modularity • Integration of external data • Overlapping / Disjoint

Multiple networks to depict protein cellular functions

PPI (12110 / 60669)Spike Biocarta Kegg Nci

Co-Expression (9912 / 1107547)

Pathway-related (8839 / 166761)

CORUM complexes (2528 / 36762)

• Does the combination of networks improves the detection of communities?

• How to combine multiple networks to identify communities?

Network aggregation

Union, Sum

Intersection

Consensus partitioning

Multiplex framework

Multiplex, Multi-layer, Multislice

Multiplex framework

• Multiplex-modularity

Protocole

• Multiplex framework

• Networks aggregation

• Consensus partition

• Modularity

• Modularity Optimization (Louvain algo)

• Multiplex-modularity

• Mx-modularity Optimization (adapted Louvain algo)

Simulations (SBMs)

Considering many networks improves the detection of communities

Simulations (SBMs)

Mx framework more efficient with heterogeneous and incomplete networks

Multiplex

SumUnionIntersection

Application to real biological networks

Pathways Co-expr PPI Complexes

• Granularity parameter from 1 to 15 • Community number, sizes, similarities

=> More balanced

Union aggregationSum aggregation

Multiplex-modularity

Resolution parameter γ

Annotated

clusters(%

)

151413121110987654321

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

Community GOBP annotations

=> More annotated

=> Multiplex framework is well suited to identify communities from multiple biological networks

Module enriched in Coffin-Siris syndrome genes

PPIPathwaysCo-

expression Complexes

SMARCC1

MLLT1

ACTG1

SMARCD1

SMARCD2

DPF3

ARID1B

MCPH1

SMARCA2

DPF2

CDX2

ZNF14

PCSK4

SMARCD3

SMARCA4

SCYL1

BAZ1B

CARM1

AFF1

PBRM1

PRMT5

ARID2

DRAP1

GATA1

ZNF2

KLF1

ACTL6B

SMARCB1

SLC10A1

UNKL

SMARCC2

ARID1A

SMARCE1

C11orf74

SMARCC1

MLLT1

ACTG1

SMARCD1

SMARCD2

DPF3

ARID1B

MCPH1

SMARCA2

DPF2

CDX2

ZNF14

PCSK4

SMARCD3

SMARCA4

SCYL1

BAZ1B

CARM1

AFF1

PBRM1

PRMT5

ARID2

DRAP1

GATA1

ZNF2

KLF1

ACTL6B

SMARCB1

SLC10A1

UNKL

SMARCC2

ARID1A

SMARCE1

C11orf74

SMARCC1

MLLT1

ACTG1

SMARCD1

SMARCD2

DPF3

ARID1B

MCPH1

SMARCA2

DPF2

CDX2

ZNF14

PCSK4

SMARCD3

SMARCA4

SCYL1

BAZ1B

CARM1

AFF1

PBRM1

PRMT5

ARID2

DRAP1

GATA1

ZNF2

KLF1

ACTL6B

SMARCB1

SLC10A1

UNKL

SMARCC2

ARID1A

SMARCE1

C11orf74

SMARCC1

MLLT1

ACTG1

SMARCD1

SMARCD2

DPF3

ARID1B

MCPH1

SMARCA2

DPF2

CDX2

ZNF14

PCSK4

SMARCD3

SMARCA4

SCYL1

BAZ1B

CARM1

AFF1

PBRM1

PRMT5

ARID2

DRAP1

GATA1

ZNF2

KLF1

ACTL6B

SMARCB1

SLC10A1

UNKL

SMARCC2

ARID1A

SMARCE1

C11orf74

Molti

https://github.com/gilles-didier/MolTiDidier et al. Accepted in PeerJ

Acknowledgments

Elisabeth Remy Laurent Tichit

Alain Guénoche Gilles Didier

José-Luis Portero

PEPS