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Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

Experimental approaches for measuring single-cell gene expression can observe each cell at only one time point, requiring computational approaches for reconstructing the dynamics of gene expression during cell fate transitions. RNA velocity is a promising computational approach for this problem, but existing inference methods fail to capture key aspects of real data, limiting their utility. To address these limitations, we developed VeloVAE, a Bayesian model for RNA velocity inference. VeloVAE uses variational Bayesian inference to estimate the posterior distribution of latent time, latent cell state, and kinetic rate parameters for each cell. Our approach addresses key limitations of previous methods by inferring a global time and cell state value for each cell; explicitly modeling the emergence of multiple cell types; incorporating prior information such as time point labels; using scalable minibatch optimization; and quantifying parameter uncertainty. We show that VeloVAE significantly outperforms previous approaches in terms of data fit and accuracy of inferred differentiation directions. VeloVAE can also capture qualitative features of expression dynamics neglected by previous methods, including late induction, early repression, transcriptional boosts, and bifurcations. These improvements allow VeloVAE to accurately model gene expression dynamics in complex biological systems, including hematopoiesis, induced pluripotent stem cell reprogramming, neurogenesis, and organogenesis. We find that the latent time automatically inferred using all cells can even outperform pseudotime inferred using manually chosen cell subsets and root cells. We further use the inferred parameters to construct cell type transition graphs and fit branching differential equation models that describe the effects of cell type bifurcations on kinetic rate parameters.