ArticleCell reports methods2023
Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome 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
- Transcriptomic advances in studies of muscle stem cell aging: From bulk to single-cell and beyond.Cell research · 2026Review
- STEER: decoupling kinetics with Spatial-Temporal Explainable Expert model for RNA velocity inference.National science review · 2026Article
- Benchmarking RNA velocity methods across 17 independent studies.Cell reports methods · 2026Article
- Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities.NPJ systems biology and applications · 2026Article
- Interpretable learning of temporal cellular dynamics from single-cell data.Cell reports methods · 2026Article
- stVCR: spatiotemporal dynamics of single cells.Nature methods · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- The pioneer factor, ETV2, regulates networks to specify the embryonic endothelial lineage.Cardiovascular research · 2025Article
- SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder.Genome biology · 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
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.Nature methods · 2024Article
- Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information.PLoS computational biology · 2024Article
- Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can predict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperforming comparable autoencoder batch correction methods that do not consider gene expression dynamics.
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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.