DeepVelo: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations

Abstract

Recent advances in single-cell RNA sequencing technology have provided unprecedented opportunities to simultaneously measure the gene expression profile and transcriptional velocity of individual cells, enabling us to sample gene regulatory network dynamics along developmental trajectories. However, traditional methods have faced challenges in modeling gene expression dynamics within individual cells due to sparse, non-linear (e.g., obligate heterodimer transcription factors), and high-dimensional measurements. Here, we present DeepVelo, a neural-network-based ordinary differential equation model that can learn non-linear, high-dimensional single-cell transcriptome dynamics and describe continuous gene expression changes within individual cells across time. We applied DeepVelo to multiple published datasets from different technical platforms and demonstrated its utility to 1) formulate transcriptome dynamics on different timescales, 2) measure the instability of cell states, and 3) identify developmental driver genes upstream of a signaling cascade. Benchmarking against state-of-the-art methods shows that DeepVelo can improve velocity field representation accuracy by at least 50% in out-of-sample cells. Further, perturbation studies revealed that single-cell dynamical systems may exhibit properties similar to those of chaotic systems. In summary, DeepVelo allows for the data-driven discovery of differential equations that delineate single-cell transcriptome dynamics. TeaserEmbedding neural networks into ordinary differential equations to model gene expression changes within single cells across time.