A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.

Motivation: Differential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges. Results: Here, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net. Availability and implementation: All the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.