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How threshold behaviour affects the use of subgraphs for network comparison.

Tiago Rito1, Zi Wang, Charlotte M Deane

  • 1Department of Statistics, University of Oxford, Oxford, UK. tiago@stats.ox.ac.uk

Bioinformatics (Oxford, England)
|September 9, 2010
PubMed
Summary

Comparing protein-protein interaction networks requires robust methods. Our study found that the Graphlet Degree Distribution Agreement (GDDA) score is unstable for current protein-protein interaction networks, indicating common models do not fit well.

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Area of Science:

  • Systems Biology
  • Network Science
  • Bioinformatics

Background:

  • Protein-protein interaction (PPI) data is abundant, organized into PPI networks.
  • Efficient and biologically meaningful methods are needed to compare these complex networks.
  • Comparing observed networks to established models using small subgraph counts is a key first step.

Purpose of the Study:

  • To evaluate the Graphlet Degree Distribution Agreement (GDDA) score for comparing protein-protein interaction networks.
  • To assess the fit of common random graph models to biological PPI networks.
  • To develop a statistically rigorous method for network model comparison.

Main Methods:

  • Employed the GraphCrunch software tool and the GDDA score.
  • Utilized non-parametric tests to assess statistical significance of model fit.
  • Compared Erdös-Rényi (ER), ER with fixed degree distribution, and 3D geometric models to PPI networks.

Main Results:

  • The GDDA score's dependency on network size (edges and vertices) must be considered.
  • None of the tested random graph models (ER, ER with fixed degree, 3D geometric) adequately fit biological PPI networks.
  • The GDDA score is unstable at graph densities relevant to current PPI networks, potentially due to threshold effects.

Conclusions:

  • Current random graph models do not accurately represent biological PPI networks.
  • The instability of the GDDA score highlights limitations in comparing networks with specific densities.
  • Threshold behavior in PPI networks may relate to their robustness and efficiency properties.