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scvelo

Estimate RNA velocity and cell state transitions from single-cell RNA-seq data.

3|Updated Apr 17, 2026
Search Tags:#single-cell RNA-seq#RNA velocity#trajectory inference#latent time#driver gene
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill scvelo-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/scvelo
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill scvelo-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

scVelo is designed to address the complex challenge of analyzing single-cell RNA-seq data by enabling the inference of cell state transitions, trajectory directions, latent time, and identification of driver genes without requiring time-course data.

Core Features & Use Cases

  • RNA Velocity Analysis: Estimate cell state transitions from unspliced/spliced mRNA dynamics.
  • Trajectory Inference: Reconstruct developmental trajectories and infer cell fate decisions.
  • Latent Time Estimation: Order cells along a pseudotime derived from splicing dynamics.
  • Driver Gene Identification: Find genes whose dynamics best explain observed trajectories.
  • Use Case: In hematopoiesis research, scVelo can help map the differentiation trajectory of hematopoietic stem cells.

Quick Start

Use scVelo to perform RNA velocity analysis on your single-cell RNA-seq data with the command: pip install scvelo

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 and cell state transitions from single-cell RNA-seq data?▼

RNA velocity is estimated by modeling unspliced and spliced mRNA dynamics. This approach identifies cell state transitions and trajectory directions from single-cell RNA-seq data without requiring time-course experimental data.

What is the best way to infer developmental trajectories and cell fate decisions?▼

Trajectory inference reconstructs developmental trajectories by analyzing splicing kinetics. It models mRNA dynamics to infer cell fate decisions, mapping continuous differentiation paths like hematopoietic stem cell development.

How do I order cells along a pseudotime using single-cell transcriptomic data?▼

Ordering cells along a pseudotime requires latent time estimation. This process derives a latent time scale from splicing dynamics to sequence cell states throughout their developmental progression.

Can I identify driver genes that explain observed single-cell trajectories?▼

Driver gene identification finds genes whose mRNA dynamics best explain observed trajectories. These genes exhibit significant splicing kinetic changes that drive cell state transitions and fate decisions.

Do I need time-course data to perform RNA velocity analysis?▼

Time-course data is not required to perform RNA velocity analysis. The method infers temporal dynamics directly from unspliced and spliced mRNA counts captured in standard single-cell RNA-seq snapshots.

Why use scvelo for single-cell biology and developmental research?▼

scvelo applies stochastic and dynamical models to mRNA splicing kinetics for single-cell biology. It enables latent time estimation and trajectory inference, solving complex developmental mapping challenges.

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