CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Cell fate decisions are orchestrated by intricate gene regulatory networks (GRNs), which govern gene expression with precise spatiotemporal control. However, accurately capturing context-specific nature of gene regulation remains challenging, particularly when integrating multi-omics data at bulk and single-cell level across diverse cellular contexts. Here, we present CellPolaris, a unified computational framework designed to decode the roles of transcription factors (TFs) in developmental processes. CellPolaris performs TF-centered GRN construction, master TF identification, and TF perturbation simulation. By leveraging transfer learning, the framework generates tissue-specific or cell-type-specific GRNs using pre-constructed high-confidence GRNs of diverse contexts and requires only transcriptomic data as input. Using these learned GRNs, CellPolaris identifies underlying master TFs critical for cell fate transitions and simulates the effects of TF perturbations on developmental processes. Benchmarking tests demonstrate the robust performance of CellPolaris in GRN construction. The efficacy of CellPolaris is supported by the significant overlap between predicted top-ranked master regulators and known TF combinations experimentally validated in cell fate conversion experiments. Furthermore, CellPolaris accurately simulates the developmental consequences of Rfx2 knockout during round spermatid differentiation. In summary, we present CellPolaris, a comprehensive framework that enables GRN construction through transfer learning, identification of key TFs driving cell fate transitions, and simulation of TF perturbations. This tool allows us to further elucidate the regulatory mechanisms underlying developmental processes and cell state transitions.