VelOT: kinetic-free RNA velocity inference via optimal transport, flow-field smoothing, and VAMP coarse-graining of cellular dynamics

Abstract

Inferring cellular dynamics from snapshot single-cell RNA sequencing remains difficult when spliced and unspliced counts are sparse or unreliable. We present VelOT, a kinetic-free RNA velocity framework that formulates dynamics as local optimal transport on the gene-expression manifold. VelOT orders cells by diffusion pseudotime, constructs overlapping spatial-temporal windows, estimates displacement vectors with entropy-regularized transport, and smooths them with a lightweight neural flow field. A downstream VAMP-based MetaFlow module learns soft meta-states and a directed PAGA-like graph, identifying initial, terminal, branching, and cycling regimes with committor probabilities. Across four real benchmarks and three synthetic topologies, VelOT outperforms scVelo, DeepVelo, and FluxMatching in cross-boundary directionality and intra-cluster coherence while remaining computationally efficient. In adult oligodendroglioma scRNA-seq, VelOT recovers stem-like to astrocyte-like and oligodendrocyte-like differentiation axes without kinetic inputs. VelOT reframes RNA velocity within scRNA-seq as a geometry and transport problem that does not require kinetic modeling.