scvelo

What this skill does

# 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
- **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 | Ac