Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures

Inferring cell type-specific gene regulatory networks (GRNs) from time-series single-cell RNA sequencing (scRNA-seq) data is challenging due to sparse temporal resolution, high dimensionality, and inherent cellular heterogeneity. We present a novel integrative framework, called PseudoGRN, that unifies multiple pseudotime inference methods, different time-lagged divergence measures, non-redundant penalized network inference, and partial correlation analysis to reconstruct directed GRNs from time-series scRNA-seq data. Applying our method to the real-world scRNA-seq dataset, we demonstrate its superior performance over existing approaches, offering a robust and interpretable tool for uncovering dynamic regulatory mechanisms in single-cell systems.