scPhyloGRN: gene regulatory network inference guided by lineage topology in single-cell developmental data

MOTIVATION: Gene regulatory network (GRN) inference from single-cell transcriptomes is complicated by expression patterns associated with both cell state and shared ancestry. Cellular phylogenies provide developmental context, but incorporating relationships among individual cells into gene-level regulatory inference remains challenging. RESULTS: We present scPhyloGRN, a supervised framework that integrates single-cell gene expression with cellular phylogeny to prioritize directed transcription factor (TF)-target candidates within the observed gene set. The framework maps cell ancestry to gene associations that guide graph construction, attention in a dual-stream encoder, and lineage-consistency regularization. We evaluated scPhyloGRN against ten methods across 17 dataset-reference settings spanning four mouse embryos and one Caenorhabditis elegans dataset. scPhyloGRN showed an overall advantage in regulatory edge prioritization, supported by additional evaluations using shared candidate spaces. Removing lineage information reduced AUPRC in the ablation analyses, while branch-level and regulator-level analyses showed improved ranking and recovery of reference-supported regulatory candidates. AVAILABILITY AND IMPLEMENTATION: Source code and analysis workflows are available at https://github.com/Maria0852/scPhyloGRN, with a versioned archive at https://doi.org/10.5281/zenodo.22793831.