RegVelo: gene-regulatory-informed dynamics of single cells

Conversely, methods that infer gene regulatory networks (GRNs) do not consider the dynamically changing nature of biological systems. To integrate these two currently disconnected fields, we present RegVelo, an end-to-end dynamic, interpretable, and actionable deep learning model that learns a joint model of splicing kinetics and gene regulatory relationships and allows us to perform in silico perturbation predictions.

When applied to datasets of the cell cycle, human hematopoiesis, and murine pancreatic endocrinogenesis, RegVelo demonstrates superior predictive power for interactions and perturbation simulations, for example, compared to methods that focus solely on dynamics or GRN inference. To leverage RegVelo’s full potential, we studied the dynamics of zebrafish neural crest development and underlying regulatory mechanisms using our Smart-seq3 dataset and shared gene expression and chromatin accessibility measurements.