Knowledge-Guided RNA Velocity Inference with Large Language Models

Understanding how cells transition between states is central to reconstructing dynamic biological processes from single-cell data. RNA velocity provides local, expression-derived estimates of transcriptional dynamics, but these signals do not explicitly encode the population-level biological structure governing plausible transitions between cell states. We therefore systematically investigate whether large language models (LLMs) can provide complementary biological reasoning for cell-state transition inference. Our benchmark reveals a clear capability boundary: LLMs are effective at identifying cell states and reasoning about state-level transitions, but are substantially less reliable at predicting fine-grained gene-level dynamics. Motivated by this complementarity, we introduce veloLLM, a knowledge-guided RNA velocity framework that preserves local expression-based dynamics while incorporating population-level transition priors to guide the organization of the velocity field. Across multiple single-cell datasets and base velocity estimators, veloLLM produces velocity fields that better align with known cell-state transitions while maintaining local dynamical coherence. Together, these results establish a principled division of labor between quantitative RNA velocity estimation and high-level biological reasoning, providing a general strategy for integrating biological knowledge into cell-state dynamics inference.