Abstract
RNA velocity-based methods estimate cellular dynamics and cell developmental trajectories based on spliced and unspliced RNA counts. Although numerous methods have been proposed, RNA velocity-based models vary greatly in their biophysical assumptions, architectures, and use cases. In this work, we introduce a new architecture, CellFlows, which incorporates self-supervised neural dimensionality reduction with the flexibility of neural-based latent time estimation into a mechanistic model, improving model interpretability and accuracy. CellFlows models splicing dynamics to infer gene and context-specific kinetic rates at single-cell resolution and correctly identifies both linear and branching cellular differentiation pathways originating from mouse embryonic stem cells.
Sei Chang
PhD Candidate
Machine learning and computational genomics researcher at Columbia University and New York Genome Center.