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scvelo

Infer RNA velocity and cell fate trajectories from single-cell RNA-seq AnnData.

Updated May 24, 2026
Search Tags:#pseudotime#scvelo#rna velocity#single-cell rna-seq#trajectory inference#annadata#driver genes
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scvelo-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/scvelo
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scvelo-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill turns single-cell RNA-seq snapshot data into inferred RNA developmental trajectories by estimating RNA velocity from unspliced and spliced mRNA dynamics, helping you recover directionality and pseudotime without time-course experiments.

Core Features & Use Cases

  • RNA velocity estimation: Model gene-specific splicing kinetics to compute per-cell velocity fields and a velocity graph for inferred transitions.
  • Trajectory direction & latent time: Compute latent time (shared pseudotime) and velocity pseudotime to order cells along putative progression.
  • Driver gene identification: Rank velocity-associated genes and use model fit metrics (e.g., fit_likelihood, fit_alpha/beta/gamma) to find genes that explain transitions.
  • Use case: Identify progenitor-to-fate trajectories in scRNA-seq datasets (e.g., differentiation or EMT-like processes) and interpret which genes drive the dynamics.

Quick Start

Run RNA velocity on your AnnData object using scVelo’s dynamical model by providing spliced/unspliced layers and UMAP coordinates, then export velocity plots and results to an output folder.

Frequently Asked Questions about scvelo

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

FAQPage Schema
How do I infer RNA velocity and cell trajectories from single-cell RNA-seq snapshot data?▼

To infer RNA velocity from single-cell RNA-seq snapshot data, you use unspliced and spliced mRNA layers within an AnnData object to model gene-specific splicing kinetics, which helps recover directionality and pseudotime without time-course experiments.

What is latent time in trajectory inference and how is it calculated using unspliced and spliced mRNA?▼

Latent time, or shared pseudotime, is a progression ordering of cells calculated by applying scVelo kinetic models to the unspliced and spliced mRNA dynamics in your AnnData layers to estimate RNA velocity.

How do I identify driver genes in scRNA-seq trajectory inference workflows?▼

You identify driver genes in scRNA-seq workflows by ranking velocity-associated genes and evaluating model fit metrics like fit_likelihood and fit_alpha/beta/gamma to find genes explaining cell fate transitions.

Do I need specific AnnData layer fields to run scVelo dynamical models for trajectory direction?▼

Yes, running scVelo dynamical models requires correctly formatted AnnData objects with specific layer fields, specifically layers['spliced'] and layers['unspliced'], alongside neighbor and moment preprocessing.

Can I use scVelo stochastic models instead of dynamical models for pseudotime ordering?▼

Yes, for pseudotime ordering you can apply scVelo stochastic models as an alternative to the dynamical model to estimate RNA velocity and compute the velocity graph for inferred cell transitions.

What limitations should I consider when estimating RNA velocity from snapshot data without time-course experiments?▼

A key limitation of estimating RNA velocity from snapshot data is the reliance on model assumptions for splicing kinetics, requiring correct spliced/unspliced AnnData layers and neighbor preprocessing to accurately infer trajectory direction.

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