Extracting Between-Pathway Models from E-MAP Interactions Using Expected Graph Compression

  • David R. Kelley
  • Carl Kingsford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Genetic interactions (such as synthetic lethal interactions) have become quantifiable on a large-scale using the epistatic miniarray profile (E-MAP) method. An E-MAP allows the construction of a large, weighted network of both aggravating and alleviating genetic interactions between genes. By clustering genes into modules and establishing relationships between those modules, we can discover compensatory pathways. We introduce a general framework for applying greedy clustering heuristics to probabilistic graphs. We use this framework to apply a graph clustering method called graph summarization to an E-MAP that targets yeast chromosome biology. This results in a new method for clustering E-MAP data that we call Expected Graph Compression (EGC). We validate modules and compensatory pathways using enriched Gene Ontology annotations and a novel method based on correlated gene expression. EGC finds a number of modules that are not found by any previous methods to cluster E-MAP data. EGC also uncovers core submodules contained within several previously found modules, suggesting that EGC can reveal the finer structure of E-MAP networks.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David R. Kelley
    • 1
  • Carl Kingsford
    • 1
  1. 1.Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer ScienceUniversity of MarylandCollege Park

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