Work

Time-Varying Gene Regulatory Networks Inference Using KL Divergence from Single Cell Data

Year: 2025

Type: conference-paper

Abstract: Correct reconstruction of dynamic gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data is essential for understanding biological processes, but remains challenging due to high-dimensionality, sparsity, and temporal heterogeneity. We propose a novel framework that integrates Kullback-Leibler divergence (KL) divergence-based temporal variation measurement with an autoregressive model and different regularization methods to infer time-varying regulatory networks from time-series scRNA-seq data. Partial correlation analysis further refines the sign of interactions (activation or inhibition). Simulation studies on a 10-gene synthetic dataset and a THP-1 monocyte differentiation dataset demonstrate that our approach accurately recovers dynamic network structures and maintains temporal consistency.

Source: International Conference on Bioinformatics and Biomedical Technology (ICBBT)

DOI: 10.1109/icbbt65815.2025.11276344

Authors: Lingling Zhang, Yunge Wang, Tong Si, Lucas Koch, Sarah Roberts, Haijun Gong

Institutions: University at Albany, State University of New York, Saint Louis University, Culver Stockton College

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences

Awards: G4851623565, G9000123583