scvelo

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: uv 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

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)