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Multiplex Biological Networks
Communities
1
BioNetVisa WS
25/11/2015
Singaporeanais.baudot@univ-amu.fr
I2M
Systems Biology
Genotypes
Phenotypes
-Omics
Networks
Large-scale Protein-protein
Interaction Networks
Interactomes
How to extract relevant
biological information ?
Identification of communities
Classes / Clusters / Modules
Protein cellular
functions
Group of tightly linked
genes/proteins
6
Inspired from P. Aloy, ECCB 2014
Protein - Structures Functional modules Interaction Networks
Comprehensiveness
Precisio...
Multiple networks to depict protein
cellular functions
PPI (12110 / 60669)
Spike Biocarta Kegg Nci
Co-Expression (9912 / 1...
• Does the combination of networks improves the detection
of communities?
• How to combine multiple networks to identify c...
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
(Louva...
Simulations (SBMs)
Considering many networks improves the detection of
communities
Simulations (SBMs)
Mx framework more efficient with heterogeneous and
incomplete networks
Multiplex
Sum
Union
Intersection
Application to real biological
networks
Pathways Co-expr PPI Complexes
• Granularity parameter from 1 to 15
• Community nu...
Union aggregation
Sum aggregation
Multiplex-modularity
Resolution parameter γ
Annotatedclusters(%)
151413121110987654321
0...
Module enriched in Coffin-Siris
syndrome genes
PPIPathways
Co-
expression Complexes
SMARCC1
MLLT1
ACTG1
SMARCD1
SMARCD2
DPF...
Molti
https://github.com/gilles-didier/MolTi
Didier et al. Accepted in PeerJ
Acknowledgments
Elisabeth Remy
Laurent Tichit
Alain Guénoche
Gilles Didier
José-Luis Portero
PEPS
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Identification of communities from Multiplex Networks

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Slides from Talk in BioNetVisa workshop in Singapore, November 2015

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Identification of communities from Multiplex Networks

  1. 1. Multiplex Biological Networks Communities 1 BioNetVisa WS 25/11/2015 Singaporeanais.baudot@univ-amu.fr I2M
  2. 2. Systems Biology Genotypes Phenotypes -Omics Networks
  3. 3. Large-scale Protein-protein Interaction Networks
  4. 4. Interactomes How to extract relevant biological information ?
  5. 5. Identification of communities Classes / Clusters / Modules Protein cellular functions Group of tightly linked genes/proteins
  6. 6. 6 Inspired from P. Aloy, ECCB 2014 Protein - Structures Functional modules Interaction Networks Comprehensiveness Precision • Topology : Density, Modularity • Integration of external data • Overlapping / Disjoint
  7. 7. 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)
  8. 8. • Does the combination of networks improves the detection of communities? • How to combine multiple networks to identify communities?
  9. 9. Network aggregation Union, Sum Intersection
  10. 10. Consensus partitioning
  11. 11. Multiplex framework Multiplex, Multi-layer, Multislice
  12. 12. Multiplex framework • Multiplex-modularity
  13. 13. Protocole • Multiplex framework • Networks aggregation • Consensus partition • Modularity • Modularity Optimization (Louvain algo) • Multiplex-modularity • Mx-modularity Optimization (adapted Louvain algo)
  14. 14. Simulations (SBMs) Considering many networks improves the detection of communities
  15. 15. Simulations (SBMs) Mx framework more efficient with heterogeneous and incomplete networks Multiplex Sum Union Intersection
  16. 16. Application to real biological networks Pathways Co-expr PPI Complexes • Granularity parameter from 1 to 15 • Community number, sizes, similarities => More balanced
  17. 17. Union aggregation Sum aggregation Multiplex-modularity Resolution parameter γ Annotatedclusters(%) 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
  18. 18. Module enriched in Coffin-Siris syndrome genes PPIPathways Co- 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
  19. 19. Molti https://github.com/gilles-didier/MolTi Didier et al. Accepted in PeerJ
  20. 20. Acknowledgments Elisabeth Remy Laurent Tichit Alain Guénoche Gilles Didier José-Luis Portero PEPS

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