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Identifying context-dependent
community structure across
multiple networks
Hyunghoon Cho, Gerald Quon, Bonnie Berger, Mano...
Modules / communities
Cellular functions are carried out by groups of
biomolecules (e.g., proteins, RNA) acting in a
coord...
Detecting changes in modules
1 2
3
1
2
3
Context
Module
1 2 Kv v v
Approaches to module detection
• Many algorithms for detecting modules in a single network
– Link clustering [Shi et al. 2...
Model description: SB
Note: each node belongs to a single module
Adjacency matrix
Model description: MMSB
[Airoldi et al., 2008]
Model description: Multi-MMSB
Learning the model
Goal: optimize model likelihood
Expectation-Maximization algorithm to deal with latent variables
Need v...
Performance metric
• Normalized mutual information (NMI)
Sequence of
structural queries
Learned
community
structure
True
c...
Synthetic data: results
Normalizedmutualinformation
Synthetic data: results
Synthetic data: results
Synthetic data: results
Asthma data (GSE19301)
Microarray profiling of peripheral blood mononuclear
cells from asthma patients at 3 different stag...
Asthma data: results
RNA decay data (GSE37451)
Microarray profiling of 70 lymphoblastoid cell lines at 5
different timepoints after transcripti...
RNA decay data: results
Summary
• We developed Multi-MMSB, a flexible way of
learning community structure over multiple
networks
• Multi-MMSB outp...
Future directions
• Extending the model:
– Directed networks
– Weighted edges
• Application to other types of biological n...
Acknowledgements
• Gerald Quon
• Prof. Bonnie Berger
• Prof. Manolis Kellis
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NetBioSIG2014-Talk by Hyunghoon Cho

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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

Published in: Science
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NetBioSIG2014-Talk by Hyunghoon Cho

  1. 1. Identifying context-dependent community structure across multiple networks Hyunghoon Cho, Gerald Quon, Bonnie Berger, Manolis Kellis MIT CSAIL ISMB Network Biology SIG July 11th, 2014
  2. 2. Modules / communities Cellular functions are carried out by groups of biomolecules (e.g., proteins, RNA) acting in a coordinated fashion. Problem: how does this structure change under a different condition?
  3. 3. Detecting changes in modules 1 2 3 1 2 3 Context Module 1 2 Kv v v
  4. 4. Approaches to module detection • Many algorithms for detecting modules in a single network – Link clustering [Shi et al. 2013], label propagation [Gregory 2010], Tensor decomposition [Anandkumar et al. 2013], mixed-membership stochastic blockmodels [Airoldi et al. 2008], etc. • Not obvious how to extend to the multiple network case: Combine networks, then detect modules likely to miss rare modules Detect modules, then combine results inconsistent module definition Multi-MMSB Jointly learns modules from all networks, allow each to be only present in a subset of networks
  5. 5. Model description: SB Note: each node belongs to a single module Adjacency matrix
  6. 6. Model description: MMSB [Airoldi et al., 2008]
  7. 7. Model description: Multi-MMSB
  8. 8. Learning the model Goal: optimize model likelihood Expectation-Maximization algorithm to deal with latent variables Need variational approximation Random restarts to alleviate local optima issue
  9. 9. Performance metric • Normalized mutual information (NMI) Sequence of structural queries Learned community structure True community structure Answers Answers Calculate mutual information [Esquivel and Rosvall, 2012]
  10. 10. Synthetic data: results Normalizedmutualinformation
  11. 11. Synthetic data: results
  12. 12. Synthetic data: results
  13. 13. Synthetic data: results
  14. 14. Asthma data (GSE19301) Microarray profiling of peripheral blood mononuclear cells from asthma patients at 3 different stages: • quiet: 394 samples • exacerbation: 125 samples • follow-up (2 weeks after exacerbation): 166 samples [Bjornsdottir et al., 2011]
  15. 15. Asthma data: results
  16. 16. RNA decay data (GSE37451) Microarray profiling of 70 lymphoblastoid cell lines at 5 different timepoints after transcription arrest: • 0 hr (before transcription arrest) • 0.5 hr • 1 hr • 2 hr • 4 hr
  17. 17. RNA decay data: results
  18. 18. Summary • We developed Multi-MMSB, a flexible way of learning community structure over multiple networks • Multi-MMSB outperformed naive methods on synthetic data • When applied to real data, Multi-MMSB identified context-specific modules that are biologically plausible
  19. 19. Future directions • Extending the model: – Directed networks – Weighted edges • Application to other types of biological networks: – Regulatory networks – PPI
  20. 20. Acknowledgements • Gerald Quon • Prof. Bonnie Berger • Prof. Manolis Kellis

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