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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.