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

Yu-Jyun Huang; Ying-Ju Lai; Chuhsing Kate Hsiao

doi:10.1093/bioadv/vbad172

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

Bioinform Adv. 2023 Nov 24;3(1):vbad172. doi: 10.1093/bioadv/vbad172. eCollection 2023.

Authors

Yu-Jyun Huang^{1

2}, Ying-Ju Lai^{2

3}, Chuhsing Kate Hsiao^{2

4

5}

Affiliations

¹ Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, United States.
² Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.
³ Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, United States.
⁴ Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.
⁵ Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei 10055, Taiwan.

Abstract

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.