PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic...

Gene regulatory networks (GRNs) are essential for understanding how genes coordinate cellular processes. Large-scale single-cell perturbation studies now offer powerful opportunities for GRN inference, yet many state-of-the-art (SOTA) methods fail to fully use interventional information. We present PSGRN, a top-performing method in the CausalBench Challenge, which integrates interventional and observational single-cell RNA sequencing data using a self-training framework with synthetic gold standards. Across eight datasets and six evaluation metrics, PSGRN consistently outperformed existing approaches. With interventional data, it achieved up to 43% higher Wasserstein distances and the lowest false omission rate in K562 compared with recent SOTA methods. Using experimentally validated regulatory interactions, PSGRN showed up to 30% gains in precision and over 100% gains in recall. These results highlight PSGRN's versatility and scalability, establishing it as a robust tool for GRN inference and biological discovery from single-cell data.