| name | sc-velocity |
| description | RNA velocity analysis for single-cell omics. Wraps scVelo to quantify spliced/unspliced kinetics, latent time, velocity graphs, and driver gene ranking with optional dynamical mode fitting. |
| version | 0.1.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["singlecell","velocity","scvelo","dynamics","kinetics"] |
| metadata | {"omicsclaw":{"domain":"singlecell","emoji":"⚡","trigger_keywords":["rna velocity","latent time","spliced","velocity graph","kinetics model"]}} |
| source_reliability | [{"source":"https://github.com/theislab/scvelo","description":"Official scVelo repository maintained by the scverse/Theis Lab team with active releases for RNA velocity pipelines.","score":0.93,"rationale":"First-party repo with 3k+ stars, peer-reviewed methods, and recent commits (Dec 2025) tracked via the GitHub project page.\n"},{"source":"https://github.com/K-Dense-AI/claude-scientific-skills","description":"Claude Scientific Skills pack that documents standardized skill templates, metadata, and orchestration guardrails.","score":0.9,"rationale":"Centralized skill repository from K-Dense AI used across Claude Scientific Skills deployments; provides consistent SKILL.md format adopted by OmicsClaw.\n"}] |
⚡ Single-Cell RNA Velocity
Quantify transcriptional dynamics by coupling spliced and unspliced matrices using scVelo's stochastic and dynamical models. This skill
ingests AnnData/loom counts, performs preconditioning (normalization, moments, phase portrait checks), computes velocities, and returns
latent time embeddings with driver gene evidence suitable for trajectory-aware downstream workflows.
When to Trigger
- User mentions "RNA velocity," "latent time," "dynamic gene programs," or "spliced/unspliced layers"
- Input AnnData includes
.layers["spliced"] and .layers["unspliced"]
- Need to compare lineage directionality following clustering/integration steps
Supported Inputs
| Format | Notes |
|---|
.h5ad | Preferred; expects layers["spliced"]/["unspliced"], obs annotations, optional var kinetic priors |
.loom | Auto-converted to AnnData; verifies layers/{spliced,unspliced} |
| Matrices | Provide --spliced/--unspliced MTX + metadata to build AnnData wrapper |
Typical Workflow
- Load + QC
import scvelo as scv
adata = scv.read("sample.h5ad")
scv.pp.filter_and_normalize(adata, min_shared_counts=30, n_top_genes=4000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
- Velocity Computation
scv.tl.velocity(adata, mode="dynamical")
scv.tl.velocity_graph(adata)
scv.tl.velocity_confidence(adata)
- Latent Time & Drivers
scv.tl.recover_dynamics(adata)
scv.tl.latent_time(adata)
scv.tl.rank_velocity_genes(adata, groupby="leiden")
- Visualization & Export
scv.pl.velocity_embedding_stream(adata, basis=, color=)
adata.write_h5ad()
