Abstract
Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity, often in combination with non-linear dimensionality reduction, have been proposed. However, interpreting their results in the light of the underlying biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with a non-linear low-dimensional dynamical system. Using dynamical systems theory, NeuroVelo can then identify genes and biological processes driving temporal cellular dynamics. We benchmark NeuroVelo against several current methods using single-cell multi-omic data, demonstrating that NeuroVelo is superior to competing methods in terms of identifying biological pathways and reconstructing evolutionary dynamics.
Competing Interest Statement
Between 2018 and 2022 NV received honoraria for lectures from Merck Serono, Pfizer, Bayer, Eli-Lilly and Servier; Research funding (Institutional) from Roche and BenevolentAI and has received paid consultancy fees from BenevolentAI. At the time of submission NV was a full-time employee of AstraZeneca Plc; any work done in relation to this article was undertaken before his current employment.