ArticlePLoS computational biology2022
Towards reliable quantification of cell state velocities.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts.PLoS computational biology · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.Bioinformatics advances · 2026Article
- Geometry-preserving vector field reconstruction of high-dimensional cell-state dynamics using ddHodge.Nature communications · 2025Article
- Identification of Distinct Topological Structures From High-Dimensional Data.bioRxiv : the preprint server for biology · 2025Article
- Exploring transcription modalities from bimodal, single-cell RNA sequencing data.NAR genomics and bioinformatics · 2024Article
- Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information.PLoS computational biology · 2024Article
- Spatial transition tensor of single cells.Nature methods · 2024Article
- A dynamical perspective: moving towards mechanism in single-cell transcriptomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Dissecting the spatiotemporal diversity of adult neural stem cells.Molecular systems biology · 2024Article
- Single-cell time series analysis reveals the dynamics of HSPC response to inflammation.Life science alliance · 2024Article
- Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.Nature methods · 2024Article
- Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates.Genome biology · 2023Article
- scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics.Genome biology · 2023Article
- Single-cell multi-omics and lineage tracing to dissect cell fate decision-making.Stem cell reports · 2023Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A few years ago, it was proposed to use the simultaneous quantification of unspliced and spliced messenger RNA (mRNA) to add a temporal dimension to high-throughput snapshots of single cell RNA sequencing data. This concept can yield additional insight into the transcriptional dynamics of the biological systems under study. However, current methods for inferring cell state velocities from such data (known as RNA velocities) are afflicted by several theoretical and computational problems, hindering realistic and reliable velocity estimation. We discuss these issues and propose new solutions for addressing some of the current challenges in consistency of data processing, velocity inference and visualisation. We translate our computational conclusion in two velocity analysis tools: one detailed method κ-velo and one heuristic method eco-velo, each of which uses a different set of assumptions about the data.
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