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.

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 2. Preprocessing 3. Velocity Estimation — Stochastic Model The stochastic model is fast and suitable for exploratory analysis: 4. Velocity Estimation — Dynamical Model (Recommended) The dynamical model fits the full splicing kinetics and is more accurate: 5. Latent Time The dynamical model enables computation of a shared latent time (pseudotime): 6. Driver Gene Analysis 7. Velocity Arrows and Pseudotime 8. PAGA Trajectory Graph Complete Workflow Script 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/