About GraphVelo

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Learning the vector field function from gene expression profile x and RNA velocity dx/dt can uncover the governing equation that drive cell fate transition Qiu et al. (Cell). Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, lacking splicing dynamics, or data of non-transcriptomic modality. On the other hand, these hidden factors will also mislead learning the governing function of cell state transition Xing. (Physical Biology).

Taking various inferred single cell RNA velocity vectors, e.g. splicing-based, metabolic labeling-based, or lineage tracing-based, as input, GraphVelo takes advantage of the nature of the low-dimensional cell state manifold to:

  1. refine the estimated RNA velocity to satisfy the tangent space requirement;

  2. infer the velocities of non-transcriptomic modalities using RNA velocities.

GraphVelo thus serves as a plugin that can be seamlessly integrated into existing RNA velocity analysis pipelines, and help process single cell data for downstream cellular dynamics analyses using methods such as dynamo Qiu et al. (Cell) and CellRank Lange et al. (Nature Methods, 2022).