scvelo

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	{"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

Core Capabilities

1. Velocity estimation - Compute RNA velocity vectors for each cell
2. Trajectory inference - Reconstruct developmental paths and cell fate transitions
3. Latent time assignment - Order cells along developmental/differentiation timelines
4. Driver gene identification - Detect genes driving state transitions
5. Gene dynamics modeling - Model transcriptional kinetics (unspliced/spliced ratios)

Key Workflows

• Developmental trajectory reconstruction from time-series scRNA-seq
• Cell fate decision point identification
• Pseudo-temporal ordering of cells without explicit timing
• Key gene discovery in differentiation processes
• Validation of developmental hypotheses

Best Practices

The resource recommends ensuring quality QC of spliced/unspliced counts, using stochastic models for high-noise datasets, validating velocity vectors with known developmental markers, and integrating trajectory inference with spatial transcriptomics for context.