Comparison of tissue/disease specific integrated networks using directed graphlet signatures

We present a novel framework for counting small sub-graph patterns in integrated genome-scale networks. An integrated network was built using the physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database.The network was filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. In this study, we apply and extend the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represent each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions. Our results show that graphlets can be used for identification of systems level differences between disease states.