NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data

Author:

Boudjelthia Idris KouadriORCID,Milite SalvatoreORCID,Kazwini Nour ElORCID,Fernandez-Mateos Javier,Valeri NicolaORCID,Huang YuanhuaORCID,Sottoriva AndreaORCID,Sanguinetti GuidoORCID

Abstract

AbstractReconstructing 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.

Publisher

Cold Spring Harbor Laboratory

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