scvelo
ResearchRNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.
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name: scvelo
description: RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.
license: BSD-3-Clause
tags: [scientific-skills, scvelo, scanpy, bioinformatics]
metadata:
skill-author: Kuan-lin Huang
-------|-----|-------------|
| adata.layers | velocity | RNA velocity per gene per cell |
| adata.layers | fit_t | Fitted latent time per gene per cell |
| adata.obsm | velocity_umap | 2D velocity vectors on UMAP |
| adata.obs | velocity_pseudotime | Pseudotime from velocity |
| adata.obs | latent_time | Latent time from dynamical model |
| adata.obs | velocity_length | Speed of each cell |
| adata.obs | velocity_confidence | Confidence score per cell |
| adata.var | fit_likelihood | Gene-level model fit quality |
| adata.var | fit_alpha | Transcription rate |
| adata.var | fit_beta | Splicing rate |
| adata.var | fit_gamma | Degradation rate |
| adata.uns | velocity_graph | Cell-cell transition probability matrix |
Velocity Models Comparison
| Model | Speed | Accuracy | When to Use |
|---|---|---|---|
stochastic | Fast | Moderate | Exploratory; large datasets |
deterministic | Medium | Moderate | Simple linear kinetics |
dynamical | Slow | High | Publication-quality; identifies driver genes |
Best Practices
- Start with stochastic mode for exploration; switch to dynamical for final analysis
- Need good coverage of unspliced reads: Short reads (< 100 bp) may miss intron coverage
- Minimum 2,000 cells: RNA velocity is noisy with fewer cells
- Velocity should be coherent: Arrows should follow known biology; randomness indicates issues
- k-NN bandwidth matters: Too few neighbors → noisy velocity; too many → oversmoothed
- Sanity check: Root cells (progenitors) should have high unspliced/spliced ratios for marker genes
- Dynamical model requires distinct kinetic states: Works best for clear differentiation processes
Troubleshooting
| Problem | Solution |
|---|---|
| Missing unspliced layer | Re-run velocyto or use STARsolo with --soloFeatures Gene Velocyto |
| Very few velocity genes | Lower min_shared_counts; check sequencing depth |
| Random-looking arrows | Try different n_neighbors or velocity model |
| Memory error with dynamical | Set n_jobs=1; reduce n_top_genes |
| Negative velocity everywhere | Check that spliced/unspliced layers are not swapped |
Additional Resources
- scVelo documentation: https://scvelo.readthedocs.io/
- Tutorial notebooks: https://scvelo.readthedocs.io/tutorials/
- GitHub: https://github.com/theislab/scvelo
- Paper: Bergen V et al. (2020) Nature Biotechnology. PMID: 32747759
- velocyto (preprocessing): http://velocyto.org/
- CellRank (fate prediction, extends scVelo): https://cellrank.readthedocs.io/
- dynamo (metabolic labeling alternative): https://dynamo-release.readthedocs.io/