scvelo

Compute RNA velocity and latent time from scRNA-seq data.

21|1|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill scvelo-ownlabai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/scvelo
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill scvelo-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

scVelo provides RNA velocity analysis to infer cell-state trajectories from snapshot single-cell RNA-seq data by modeling spliced and unspliced mRNA dynamics, enabling researchers to predict future cell states and differentiation directions.

Core Features & Use Cases

  • Dynamic velocity estimation (stochastic and dynamical modes) to infer transcriptional kinetics from spliced/unspliced counts.
  • Latent time and velocityGraph computation to order cells along developmental trajectories.
  • Driver-gene identification and visualization to highlight genes driving state transitions.
  • Seamless integration with Scanpy workflows on AnnData objects to extend standard single-cell analyses.

Quick Start

Load your AnnData with spliced and unspliced layers, then run the dynamical velocity workflow to obtain latent time and driver genes.

Frequently Asked Questions about scvelo

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

FAQPage Schema
How do I compute RNA velocity from spliced and unspliced counts in scRNA-seq data?

RNA velocity is computed by applying stochastic or dynamical models to spliced and unspliced counts within an AnnData object to infer transcriptional kinetics and predict future cell states.

What is latent time estimation for single-cell trajectory inference?

Latent time estimation orders cells along developmental trajectories by computing a velocityGraph from mRNA dynamics, revealing the actual temporal progression of cell states from snapshot scRNA-seq data.

Do I need spliced and unspliced mRNA layers to infer cell-state trajectories?

Yes, inferring cell-state trajectories requires an AnnData object containing spliced and unspliced layers, as scVelo models transcriptional kinetics by comparing these counts to determine differentiation directions.

Can I integrate RNA velocity analysis with my existing Scanpy workflow?

RNA velocity analysis integrates seamlessly with Scanpy workflows on AnnData objects, extending standard single-cell analyses by computing neighbors and moments before running the dynamical velocity workflow.

How do I identify driver genes from single-cell RNA velocity results?

Driver genes are identified after computing dynamical velocity by analyzing gene-specific dynamics to highlight and visualize the genes driving cell state transitions along the inferred trajectories.

What are the limitations of using dynamical mode for trajectory inference?

Dynamical mode trajectory inference requires preprocessing steps to compute neighbors and moments from the spliced and unspliced layers, demanding a Python environment with scvelo and Scanpy properly configured.