Computational Methods for Single-Cell Network Biology Analysis

Abstract

The traditional transcriptome profiling methods fail to capture cellular heterogeneity, hindering the inference of cell type-specific gene regulatory networks. Single-cell RNA sequencing (scRNA-seq) emerged as a breakthrough, revolutionizing our understanding of cellular intricacies and disease mechanisms. However, challenges persist in harnessing its full potential, including data integration, dimensionality reduction, and network construction. This thesis explores methodologies for extracting biological insights from scRNA-seq data through network-based analysis. It introduces techniques to identify interconnected gene subnetworks crucial for understanding single-cell development trajectories, uncovering differential regulatory mechanisms between cell types, and predicting potential drug repurposing candidates targeting genes within these networks. These methodologies aim to advance our understanding of cellular heterogeneity, disease pathogenesis, and personalized therapeutic interventions at the single-cell level.