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scvelo

Analyzes scVelo RNA velocity dynamics in AnnData datasets to infer cell-state transitions and trajectories.

Updated Jul 1, 2026
Search Tags:#single-cell#scanpy#scvelo#rna velocity#trajectory inference#latent time#driver genes
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scvelo-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/scvelo
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scvelo-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvelo, scanpy, numpy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you analyze single-cell RNA-seq data to detect directional cell state changes, distinguish healthy from unstable transitions, and understand when gene expression dynamics indicate emerging lineage paths or failed progression.

Core Features & Use Cases

  • Velocity estimation: Computes RNA velocity from spliced and unspliced counts to reveal the likely next state of each cell.
  • Trajectory inference: Estimates latent time and velocity-informed transitions to map developmental direction across cell populations.
  • Driver gene analysis: Ranks genes that best explain observed dynamics and highlights markers associated with differentiation or repression.
  • Visualization and quality checks: Produces velocity streams, arrow plots, pseudotime views, and confidence metrics for interpretation and validation.
  • Use case: A researcher can load a processed AnnData object, run velocity modeling, and generate plots and gene rankings to study how a stem-cell population differentiates over time.

Quick Start

Use the scvelo skill to analyze my AnnData object for RNA velocity, latent time, and driver genes, then summarize the key results and recommended visualizations.

Frequently Asked Questions about scvelo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate RNA velocity from spliced and unspliced counts in single-cell data?▼

You can estimate RNA velocity from spliced and unspliced counts by applying stochastic or dynamical models to an AnnData object, which computes the likely next cell state for trajectory inference.

Can I use Scanpy with scVelo for trajectory inference on my single-cell dataset?▼

Yes, scVelo works directly with Scanpy and AnnData objects containing spliced and unspliced layers, allowing you to preprocess data and infer cell state transitions seamlessly.

How does latent time estimation work for mapping single-cell developmental direction?▼

Latent time estimation uses velocity-informed transitions to map developmental direction across cell populations, placing cells along a pseudotime axis based on their predicted RNA velocity dynamics.

Do I need an AnnData object with spliced and unspliced layers to compute RNA velocity?▼

Yes, an AnnData object with spliced and unspliced count layers is required to fit velocity models and successfully estimate single-cell RNA velocity dynamics.

What is the best way to identify driver genes associated with cell differentiation?▼

Ranking driver genes involves analyzing single-cell RNA velocity dynamics to highlight specific markers that best explain observed differentiation or repression transitions.

Why are my RNA velocity results showing unstable or failed cell state transitions?▼

Unstable or failed cell state transitions in RNA velocity indicate disrupted gene expression dynamics, distinguishing healthy progression from failed differentiation paths.

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