STAVelo: deciphering spatiotemporal cellular dynamics via spatial RNA velocity inference from spatial transcriptomics data

Abstract RNA velocity is a powerful tool for deciphering cellular transcriptional dynamics from transcriptomics datasets. However, existing methods have several limitations: some do not model cellular spatial location information, some rely on cell-agnostic constant transcriptional kinetic rates, and some have not been validated for generalization across a wide range of datasets. To this end, we present STAVelo, a graph attention encoder-decoder framework that models spatial information in spatial transcriptomics (ST) data and infers RNA velocity from neural representations of transcriptional kinetic parameters. In quantitative comparisons of multiple datasets from diverse species and platforms, STAVelo outperforms previous methods in capturing cellular migration and development dynamics. In human and mouse brain datasets from different platforms, STAVelo accurately inferred the inside-out migration pattern of neurons across cortical layers. Analysis of mouse kidney and chicken heart datasets captured organ-specific developmental gradients during organ development. Analysis of mouse embryo and placenta datasets with multiple time points uncovered spatiotemporal dynamics from existing states to newly established states. In short, STAVelo provides a spatial view of cellular state transitions and deeper insights into spatiotemporal cellular dynamics in complex tissue structures.