Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks

• Main Authors: Liu, Wenting, Rajapakse, Jagath Chandana
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering; Gene Expressions; Gene Regulatory Network (GRN)
• Online Access: https://hdl.handle.net/10356/105990; http://hdl.handle.net/10220/48815; http://dx.doi.org/10.1186/s12918-019-0695-x
• Institution: Nanyang Technological University

Background: Systematic fusion of multiple data sources for Gene Regulatory Networks [GRN] inference remains a key challenge in systems biology. We incorporate information from protein-protein interaction networks [PPIN] into the process of GRN inference from gene expression [GE] data. However, existing PPIN remain sparse and transitive protein interactions can help predict missing protein interactions. We therefore propose a systematic probabilistic framework on fusing GE data and transitive protein interaction data to coherently build GRN. Results: We use a Gaussian Mixture Model [GMM] to soft-cluster GE data, allowing overlapping cluster memberships. Next, a heuristic method is proposed to extend sparse PPIN by incorporating transitive linkages. We then propose a novel way to score extended protein interactions by combining topological properties of PPIN and correlations of GE. Following this, GE data and extended PPIN are fused using a Gaussian Hidden Markov Model [GHMM] in order to identify gene regulatory pathways and refine interaction scores that are then used to constrain the GRN structure. We employ a Bayesian Gaussian Mixture [BGM] model to refine the GRN derived from GE data by using the structural priors derived from GHMM. Experiments on real yeast regulatory networks demonstrate both the feasibility of the extended PPIN in predicting transitive protein interactions and its effectiveness on improving the coverage and accuracy the proposed method of fusing PPIN and GE to build GRN. Conclusion: The GE and PPIN fusion model outperforms both the state-of-the-art single data source models [CLR, GENIE3, TIGRESS] as well as existing fusion models under various constraints.