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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.
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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. 📦 Ships with scientific-agent-skills
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scvelo.SKILL.md
---
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
metadata : {"version": "1.0", "skill-author": "Kuan-lin Huang"}
---
# scVelo — RNA Velocity Analysis
## Overview
scVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.
**Installation:** `pip install scvelo`
**Key resources:**
- Documentation: https://scvelo.readthedocs.io/
- GitHub: https://github.com/theislab/scvelo
- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759
## When to Use This Skill
Use scVelo when:
- **Trajectory inference from snapshot data**: Determine which direction cells are differentiating
- **Cell fate prediction**: Identify progenitor cells and their downstream fates
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- **Driver gene identification**: Find genes whose dynamics best explain observed trajectories
- **Developmental biology**: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions
- **Latent time estimation**: Order cells along a pseudotime derived from splicing dynamics
- **Complement to Scanpy**: Add directional information to UMAP embeddings
## Prerequisites
scVelo requires count matrices for both **unspliced** and **spliced** RNA. These are generated by:
1. **STARsolo** or **kallisto|bustools** with `lamanno` mode
2. **velocyto** CLI: `velocyto run10x` / `velocyto run`
3. **alevin-fry** / **simpleaf** with spliced/unspliced output
Data is stored in an `AnnData` object with `layers["spliced"]` and `layers["unspliced"]`.
## Standard RNA Velocity Workflow
### 1. Setup and Data Loading
```python
import scvelo as scv
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
# Configure settings
scv.settings.verbosity = 3 # Show computation steps
scv.settings.presenter_view = True
scv.settings.set_figure_params('scvelo')
# Load data (AnnData with spliced/unspliced layers)
# Option A: Load from loom (velocyto output)
adata = scv.read("cellranger_output.loom", cache=True)
# Option B: Merge velocyto loom with Scanpy-processed AnnData
adata_processed = sc.read_h5ad("processed.h5ad") # Has UMAP, clusters
adata_velocity = scv.read("velocyto.loom")
adata = scv.utils.merge(adata_processed, adata_velocity)
# Verify layers
print(adata)
# obs × var: N × G
# layers: 'spliced', 'unspliced' (required)
# obsm['X_umap'] (required for visualization)
```
### 2. Preprocessing
```python
# Filter and normalize (follows Scanpy conventions)
scv.pp.filter_and_normalize(
adata,
min_shared_counts=20, # Minimum counts in spliced+unspliced
n_top_genes=2000 # Top highly variable genes
)
# Compute first and second order moments (means and variances)
# knn_connectivities must be computed first
sc.pp.neighbors(adata, n_neighbors=30, n_pcs=30)
scv.pp.moments(
adata,
n_pcs=30,
n_neighbors=30
)
```
### 3. Velocity Estimation — Stochastic Model
The stochastic model is fast and suitable for exploratory analysis:
```python
# Stochastic velocity (faster, less accurate)
scv.tl.velocity(adata, mode='stochastic')
scv.tl.velocity_graph(adata)
# Visualize
scv.pl.velocity_embedding_stream(
adata,
basis='umap',
color='leiden',
title="RNA Velocity (Stochastic)"
)
```
### 4. Velocity Estimation — Dynamical Model (Recommended)
The dynamical model fits the full splicing kinetics and is more accurate:
```python
# Recover dynamics (computationally intensive; ~10-30 min for 10K cells)
scv.tl.recover_dynamics(adata, n_jobs=4)
# Compute velocity from dynamical model
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
```
### 5. Latent Time
The dynamical model enables computation of a shared latent time (pseudotime):
```python
# Compute latent time
scv.tl.latent_time(adata)
# Visualize latent time on UMAP
scv.pl.scatter(
adata,
color='latent_time',
color_map='gnuplot',
size=80,
title='Latent time'
)
# Identify top genes ordered by latent time
top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(
adata,
var_names=top_genes,
sortby='latent_time',
col_color='leiden',
n_convolve=100
)
```
### 6. Driver Gene Analysis
```python
# Identify genes with highest velocity fit
scv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)
df = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])
print(df.head(10))
# Speed and coherence
scv.tl.velocity_confidence(adata)
scv.pl.scatter(
adata,
c=['velocity_length', 'velocity_confidence'],
cmap='coolwarm',
perc=[5, 95]
)
# Phase portraits for specific genes
scv.pl.velocity(adata, ['Cpe', 'Gnao1', 'Ins2'],
ncols=3, figsize=(16, 4))
```
### 7. Velocity Arrows and Pseudotime
```python
# Arrow plot on UMAP
scv.pl.velocity_embedding(
adata,
arrow_length=3,
arrow_size=2,
color='leiden',
basis='umap'
)
# Stream plot (cleaner visualization)
scv.pl.velocity_embedding_stream(
adata,
basis='umap',
color='leiden',
smooth=0.8,
min_mass=4
)
# Velocity pseudotime (alternative to latent time)
scv.tl.velocity_pseudotime(adata)
scv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')
```
### 8. PAGA Trajectory Graph
```python
# PAGA graph with velocity-informed transitions
scv.tl.paga(adata, groups='leiden')
df = scv.get_df(adata, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
# Plot PAGA with velocity
scv.pl.paga(
adata,
basis='umap',
size=50,
alpha=0.1,
min_edge_width=2,
node_size_scale=1.5
)
```
## Complete Workflow Script
```python
import scvelo as scv
import scanpy as sc
def run_rna_velocity(adata, n_top_genes=2000, mode='dynamical', n_jobs=4):
"""
Complete RNA velocity workflow.
Args:
adata: AnnData with 'spliced' and 'unspliced' layers, UMAP in obsm
n_top_genes: Number of top HVGs for velocity
mode: 'stochastic' (fast) or 'dynamical' (accurate)
n_jobs: Parallel jobs for dynamical model
Returns:
Processed AnnData with velocity information
"""
scv.settings.verbosity = 2
# 1. Preprocessing
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=n_top_genes)
if 'neighbors' not in adata.uns:
sc.pp.neighbors(adata, n_neighbors=30)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
# 2. Velocity estimation
if mode == 'dynamical':
scv.tl.recover_dynamics(adata, n_jobs=n_jobs)
scv.tl.velocity(adata, mode=mode)
scv.tl.velocity_graph(adata)
# 3. Downstream analyses
if mode == 'dynamical':
scv.tl.latent_time(adata)
scv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)
scv.tl.velocity_confidence(adata)
scv.tl.velocity_pseudotime(adata)
return adata
```
## Key Output Fields in AnnData
After running the workflow, the following fields are added:
| Location | Key | Description |
|----------|-----|-------------|
| `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/
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