Geometric Learning for Single-Cell RNA Velocity Modeling

Biological cells undergo state transitions, measurable with single-cell RNA sequencing. RNA velocity estimates gene expression change rates, forming velocity vectors. These enable the construction of vector fields for predicting cellular dynamics. We introduce an innovative method for computing these vector fields from existing RNA velocity vectors.