Agent skill · research science · k-dense-ai
scvelo
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.
Why this skill is useful
Provides domain-specific knowledge for RNA velocity analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires anndata, numpy, pandas, scanpy, scvelo installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
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: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scvelo