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This study aims to enhance RNA velocity inference for modeling gene expression dynamics during cell fate transitions.

Bayesian inference of RNA velocity incorporating timepoints, lineage bifurcations, and count data

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Bayesian inference improves RNA velocity analysis in hematopoiesis, suggesting enhanced modeling of gene expression dynamics.

This study aims to enhance RNA velocity inference for modeling gene expression dynamics during cell fate transitions.
Developed VeloVAE, a Bayesian model for RNA velocity inference
Utilized variational Bayesian inference to estimate latent time and kinetic rate parameters
Incorporated prior distributions and modeled lineage bifurcations with branching differential equations
VeloVAE significantly outperforms existing methods in data fit and accuracy
Accurate modeling of gene expression dynamics in complex biological systems like hematopoiesis
Latent time inferred from all cells outperforms traditional pseudotime methods

Gu et al. (2026) studied this question.

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