Verified:GitHub

scvelo

Analyze RNA velocity in single-cell RNA sequencing data with scVelo.

Updated Mar 10, 2026

Search Tags:#cell differentiation #scvelo #rna velocity #single-cell rna-seq #trajectory inference #gene expression dynamics

Ready to run

npx skills add https://github.com/Yezez9/Research-Agent --skill scvelo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scvelo
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/scvelo
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill scvelo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of understanding dynamic biological processes like cell differentiation and development from static single-cell RNA sequencing data. It allows researchers to infer future cell states and identify key regulatory genes.

Core Features & Use Cases

RNA Velocity Estimation: Computes the rate of change of gene expression based on spliced and unspliced mRNA counts.
Trajectory Inference: Infers the direction of cell differentiation and developmental trajectories.
Latent Time Calculation: Estimates a pseudotime that reflects the progression through a biological process.
Driver Gene Identification: Identifies genes that are most influential in driving cell fate decisions.
Use Case: Analyze a single-cell RNA-seq dataset of developing stem cells to predict which cell types they will differentiate into and identify the genes that control this transition.

Quick Start

Run the RNA velocity analysis workflow on your AnnData object with spliced and unspliced layers.

Frequently Asked Questions about scvelo

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

FAQPage Schema

How do I calculate RNA velocity for single-cell RNA-seq data?▼

RNA velocity is calculated by analyzing spliced and unspliced mRNA counts to estimate the rate of gene expression change. This Skill uses the scVelo package to compute these dynamics and infer future cell states from static scRNA-seq data.

Can I infer cell differentiation trajectories using Scanpy?▼

Yes, this Skill integrates with Scanpy for preprocessing and visualization to infer cell differentiation trajectories. It leverages spliced and unspliced mRNA dynamics within your existing single-cell analysis workflow to determine trajectory directions.

What is latent time estimation in single-cell trajectory analysis?▼

Latent time estimation calculates a pseudotime that reflects a cell's progression through a dynamic biological process. This Skill computes latent time using scVelo to model gene expression dynamics and map out developmental timelines.

How do I identify driver genes from spliced and unspliced mRNA dynamics?▼

Driver genes are identified by analyzing spliced and unspliced mRNA dynamics to find genes most influential in cell fate decisions. This Skill uses scVelo to pinpoint key regulatory genes driving cell state transitions.

Do I need an AnnData object with spliced and unspliced layers for RNA velocity analysis?▼

Yes, RNA velocity analysis requires an AnnData object containing spliced and unspliced mRNA layers. These layers are essential for scVelo to compute gene expression rates and infer trajectory directions accurately.