DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics

1 Abstract

The introduction of RNA velocity in single-cell studies has opened new ways of examining cell differentiation and tissue development. Existing RNA velocity estimation methods are based on strong assumptions of either complete observation of cells in steady states or a predefined dynamics pattern parameterized by constant coefficients. These assumptions are violated in complex and heterogenous single-cell sequencing datasets and thus limit the application of these techniques. Here we present DeepVelo, a novel method that predicts the cell-specific dynamics of splicing kinetics using Graph Convolution Networks (GCNs). DeepVelo generalizes RNA velocity to cell populations containing time-dependent kinetics and multiple lineages, which are common in developmental and pathological systems. We applied DeepVelo to disentangle multifaceted kinetics in the processes of dentate gyrus neurogenesis, pancreatic endocrinogenesis, and hindbrain development. DeepVelo infers time-varying cellular rates of transcription, splicing and degradation, recovers each cell’s stage in the underlying differentiation process and detects putative driver genes regulating these processes. DeepVelo relaxes the constraints of previous techniques and facilitates the study of more complex differentiation and lineage decision events in heterogeneous single-cell RNA sequencing data.