ArticleProceedings of the National Academy of Sciences of the United States of America2021
Representation learning of RNA velocity reveals robust cell transitions.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
- An imputation method for single-cell RNA sequencing data based on a parallel gated recurrent unit neural network.Quantitative biology (Beijing, China) · 2026Article
- FlowMap: Geometry-Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories.bioRxiv : the preprint server for biology · 2026Article
- Article
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent.Molecular systems biology · 2026Article
- ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.Nature communications · 2026Article
- Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts.PLoS computational biology · 2026Article
- Predicting trajectories of illness using RNA velocity of whole blood.Nature communications · 2026Observational
- Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.Molecular systems biology · 2026Article
- Benchmarking RNA velocity methods across 17 independent studies.Cell reports methods · 2026Article
- Interpretable learning of temporal cellular dynamics from single-cell data.Cell reports methods · 2026Article
- Bayesian inference of RNA velocity incorporating timepoints, lineage bifurcations, and count data.PLoS computational biology · 2026Article
- Multi-omic relay velocity modeling uncovers dynamic chromatin-transcription regulation across cell states.Nature communications · 2025Article
- SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder.Genome biology · 2025Article
- GraphVelo allows for accurate inference of multimodal velocities and molecular mechanisms for single cells.Nature communications · 2025Article
- spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.Genome biology · 2025Article
- TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference.Nature communications · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- diffGEK: differential gene expression kinetics.Bioinformatics (Oxford, England) · 2025Article
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2 authors.
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Abstract
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present Velocity Autoencoder (VeloAE), a tailored representation learning method, to learn a low-dimensional representation of RNA velocity on which cellular transitions can be robustly estimated. On various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture expected cellular differentiation in different biological systems. VeloAE, therefore, enhances the usefulness of RNA velocity for studying a wide range of biological processes.
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