lamm-mit 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
- Velocity estimation - Compute RNA velocity vectors for each cell
- Trajectory inference - Reconstruct developmental paths and cell fate transitions
- Latent time assignment - Order cells along developmental/differentiation timelines
- Driver gene identification - Detect genes driving state transitions
- 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.