Extracting between-pathway models from E-MAP interactions using expected graph compression

Authors:
David R. Kelley;Carl Kingsford
Affiliations:
Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland, College Park;Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland, College Park
Venue:
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Year:
2010

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Abstract

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.