FateDriver uses weakly supervised dual-view graph learning to rank reprogramming regulators

[bioRxiv] Sung et al. present FateDriver, a weakly supervised dual-view graph neural network that jointly represents cells and genes from single-cell transcriptomes, treats experimentally delivered factors as sparse gene-level labels, and ranks remaining genes for a specified source-to-target transition under a positive-unlabeled formulation, according to the preprint. The authors said FateDriver ranked first or second among six methods in recovering externally curated reference genes across two OSKM datasets and one GMT dataset, exceeded a supervision-matched non-graph model despite weaker co-expression with the delivered factors, and achieved the highest recovery of protein targets of pluripotency-inducing chemical cocktails in both OSKM datasets. Holding either the source or the endpoint fixed yielded distinct candidate programs, including a DDX58-interferon axis associated with cells lacking endogenous pluripotency markers, they said. Applied to candidate source-target pairs, the framework prioritized distinct candidate origins for two in vivo partially reprogrammed intestinal populations.