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scvelo

Analyze single-cell RNA-seq splicing dynamics to infer cell state transitions.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#trajectory#single-cell#scvelo#rna-velocity#latent-time#velocity-graph
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scvelo-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scvelo
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scvelo-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

RNA velocity analysis with scVelo enables researchers to infer cell state transitions in single-cell RNA-seq data by modeling splicing kinetics between unspliced and spliced transcripts, helping reveal developmental trajectories and gene dynamics.

Core Features & Use Cases

  • Trajectory inference: Reconstruct lineage directionality from snapshot scRNA-seq data.
  • Latent time and driver genes: Estimate cell-specific latent time and identify genes driving transitions.
  • Workflow integration: Works with Scanpy/CellRank pipelines to augment trajectory analysis and fate prediction.

Quick Start

Run the RNA velocity workflow on your AnnData containing spliced and unspliced layers to generate velocity graphs and downstream visualizations.

Frequently Asked Questions about scvelo

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

FAQPage Schema
How do I infer cell state transitions from single-cell RNA-seq data?▼

RNA velocity analysis with scVelo infers cell state transitions in single-cell RNA-seq data by modeling splicing kinetics between unspliced and spliced transcripts to reveal developmental trajectories and gene dynamics.

What do I need to run RNA velocity analysis on my AnnData object?▼

You need an AnnData object containing spliced and unspliced count matrices. The workflow uses scVelo, Scanpy, numpy, and matplotlib to process these layers and generate velocity graphs.

How does scVelo estimate latent time and identify driver genes?▼

scVelo estimates cell-specific latent time and identifies driver genes by applying stochastic or dynamical models to unspliced and spliced mRNA dynamics within your single-cell dataset.

Can I use scVelo trajectory inference with my existing Scanpy pipeline?▼

Yes, scVelo integrates directly with Scanpy and CellRank pipelines. It augments trajectory analysis and fate prediction by adding velocity graphs and latent time estimations to your existing workflow.

What is the best way to reconstruct lineage directionality from snapshot scRNA-seq data?▼

Trajectory inference with scVelo reconstructs lineage directionality from snapshot scRNA-seq data by calculating RNA velocity vectors from unspliced and spliced transcript ratios.

Why does scVelo require both spliced and unspliced count matrices?▼

scVelo requires spliced and unspliced count matrices because its RNA velocity analysis models the kinetic rates of transcription, splicing, and degradation to predict future cell states.

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