BoYueGRN: Zero-shot causal discovery of directed gene regulatory networks from single-cell transcriptomes via amortized inference over synthetic structural causal models
Gene regulatory network (GRN) inference from single-cell RNA-seq conventionally relies on per-dataset optimization. Existing tools must be refit for every new dataset, and the majority fail to infer causal regulatory directions. Here we present BoYueGRN, an amortized causal discovery framework trained exclusively on 10,000 synthetic structural causal models. For any unseen dataset, a single forward pass returns edge probabilities and regulatory directions, while TF-centric sliding windows with asymmetric fusion extend this fixed-size model to full-transcriptome coverage. BoYueGRN demonstrates strong zero-shot performance across BEELINE benchmarks. On two independent genome-wide CRISPRi Perturb-seq screens, directional accuracy on retained edges reaches 0.86 and 0.95. Reconstructed cell-type- and stage-specific GRN dynamics across five diseases spanning more than 270,000 cells yield experimentally testable biological hypotheses. BoYueGRN reframes directed GRN inference as a train-once, reuse-across-datasets paradigm. By decoupling network reconstruction from per-dataset optimization, this paradigm opens the door to systematic, atlas-scale mapping of regulatory dynamics across human diseases.