ArticleNature methods2024
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 1 of them a synthesis that pooled it.
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Who cites it
87 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integration of GWAS and single-cell analysis for psoriasis identifies disease-associated cell subtypes and therapeutic targets.Nature communications · 2026Pooled it
- Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- FlowMap: Geometry-Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories.bioRxiv : the preprint server for biology · 2026Article
- Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- PseudoVelo: Inferring Gene Expression Derivatives Along Pseudotime as Pseudo-Velocity.International journal of molecular sciences · 2026Article
- A dependency-aware deep generative model for inferring RNA velocity from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent.Molecular systems biology · 2026Article
- Article
- Late-stage dedifferentiation and epigenetic memory of cancer stem cells in hepatocellular carcinoma.Journal of translational medicine · 2026Article
- Single-cell transcriptomics reveals differences between chorionic and basal plate cytotrophoblasts and 2D-cultured trophoblast stem cells.Communications biology · 2026Article
- ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.Nature communications · 2026Article
- Critical illness expands a transcriptionally distinct hypometabolic CD8bioRxiv : the preprint server for biology · 2026Article
- Spatially-Resolved Multiomic Atlas of Leiomyosarcoma Identifies Two Clinically Relevant Epigenetically-Driven Cell States.bioRxiv : the preprint server for biology · 2026Article
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- FOXP1 is differentially active during development of murine vasopressin and oxytocin magnocellular neurons.iScience · 2026Article
- Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.Molecular genetics and genomics : MGG · 2026Article
- STEER: decoupling kinetics with Spatial-Temporal Explainable Expert model for RNA velocity inference.National science review · 2026Article
- Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.Molecular systems biology · 2026Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.