Abstract
Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein–protein interaction (PPI) data enables the identification of protein complexes computationally. Although a great number of computational methods have been proposed to identify protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply co-complex relationships, taking into account the signs of PPIs can benefit the identification of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing methods are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model for identifying protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that our PS-SNC model can achieve better performance than other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.
•A novel partially shared singed network clustering (PS-SNC) model is proposed for joint clustering of multiple signed protein-protein interaction (PPI) networks.•Our model is able to identify common complexes shared across all PPI networks as well as unique complexes specific to individual PPI networks.•Our model can take into account the sign information of the PPIs when detecting protein complexes.•Experiment results on synthetic datasets demonstrate the superiority of our PS-SNC model over other state-of-the-art protein complex detection methods.•By applying our model on two real-world PPI networks, we can identify protein complexes associated with cancer-induced mutations.