ArticleNature biotechnology2023
Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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82 citing papers in PubMed.
- Predictive control of human pancreatic cell fate using a digital model ofbioRxiv : the preprint server for biology · 2026Article
- Article
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.Briefings in bioinformatics · 2026Article
- FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.Bioinformatics (Oxford, England) · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells.bioRxiv : the preprint server for biology · 2026Article
- ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.Nature communications · 2026Article
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts.PLoS computational biology · 2026Article
- Combined single-cell profiling of chromatin-transcriptome and splicing across brain cell types, regions and disease state.Nature biotechnology · 2026Article
- Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions.bioRxiv : the preprint server for biology · 2026Article
- CRAK-Velo: chromatin accessibility kinetics integration improves RNA velocity estimation.Genome biology · 2026Article
- Topological velocity inference from spatial transcriptomic data.Nature biotechnology · 2026Article
- Spatially decoding genotype-associated epigenetic landscapes in human lymphoma FFPE tissues via epi-Patho-DBiT.Nature communications · 2026Article
- Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis.bioRxiv : the preprint server for biology · 2026Article
- Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.Genome biology · 2026Article
- Specification of the interstitial cell lineage in the fetal mouse ovary requires balanced Notch signaling.bioRxiv : the preprint server for biology · 2026Article
22 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub ( https://github.com/welch-lab/MultiVelo ).
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