ArticleGenome biology2023
Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- Article
- 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
- A velocity-informed framework for resolving functional stratification in rare human stem cells.Leukemia · 2026Article
- Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts.PLoS computational biology · 2026Article
- Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.Molecular genetics and genomics : MGG · 2026Article
- Predicting trajectories of illness using RNA velocity of whole blood.Nature communications · 2026Observational
- Benchmarking RNA velocity methods across 17 independent studies.Cell reports methods · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- Seq-Scope-eXpanded: spatial omics beyond optical resolution.Nature communications · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.Bioinformatics advances · 2026Article
- Spatial RNA velocity reveals cellular state transitions and prognostic markers in the melanoma microenvironment.Frontiers in oncology · 2026Article
- Accurate quantification of spliced and unspliced transcripts for single-cell RNA sequencing with tidesurf.PloS one · 2026Article
- Fundamental errors in RNA velocity arising from the omission of cell growth.bioRxiv : the preprint server for biology · 2025Article
- Reconstructing Waddington's landscape from data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Specification and Functional Maturation of Sub-Cerebral Projection Neurons Derived from Human Induced Pluripotent Stem Cells.Stem cells and development · 2025Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundRNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflects known features of the system. However, the limitations of RNA velocity estimates are still not well understood.
resultsWe analyze the impact of different steps in the RNA velocity workflow on direction and speed. We consider both high-dimensional velocity estimates and low-dimensional velocity vector fields mapped onto an embedding. We conclude the transition probability method for mapping velocity estimates onto an embedding is effectively interpolating in the embedding space. Our findings reveal a significant dependence of the RNA velocity workflow on smoothing via the k-nearest-neighbors (k-NN) graph of the observed data. This reliance results in considerable estimation errors for both direction and speed in both high- and low-dimensional settings when the k-NN graph fails to accurately represent the true data structure; this is an unknown feature of real data. RNA velocity performs poorly at estimating speed in both low- and high-dimensional spaces, except in very low noise settings. We introduce a novel quality measure that can identify when RNA velocity should not be used.
conclusionsOur findings emphasize the importance of choices in the RNA velocity workflow and highlight critical limitations of data analysis. We advise against over-interpreting expression dynamics using RNA velocity, particularly in terms of speed. Finally, we emphasize that the use of RNA velocity in assessing the correctness of a low-dimensional embedding is circular.
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Registered trials
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