scvi-tools

Run scVI deep generative models on single-cell omics data.

3|Updated Apr 17, 2026

Search Tags:#deep learning #single-cell #batch correction #differential expression #omics

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill scvi-tools-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/scvi-tools
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill scvi-tools-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torchlightning, anndata, scanpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides advanced modeling for single-cell omics data, addressing challenges like batch effects, differential expression, and multi-modal integration with deep learning techniques.

Core Features & Use Cases

Deep Generative Models: Offers models like scVI for unsupervised analysis, scANVI for semi-supervised annotation, and VeloVI for RNA velocity analysis.
Batch Correction & Integration: Handles technical variation and integrates diverse data types like RNA-seq, ATAC-seq, and spatial transcriptomics.
Use Case: Imagine you have single-cell RNA-seq data with batch effects. Use this Skill to perform batch correction and differential expression analysis, leading to more reliable insights.

Quick Start

Run the scvi-tools skill on your single-cell RNA-seq data with the following command:

scvi-tools run scVI --data your_data.h5ad

Frequently Asked Questions about scvi-tools

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

FAQPage Schema

How do I perform batch correction on single-cell RNA-seq data?▼

Batch correction for single-cell RNA-seq data is handled using deep generative models like scVI to correct technical variation and extract reliable biological signals. You can run the scVI model directly on your h5ad data file to resolve batch effects.

Can I integrate multi-modal single-cell omics data like ATAC-seq and spatial transcriptomics?▼

Multi-modal integration for single-cell omics data supports combining diverse data types like RNA-seq, ATAC-seq, and spatial transcriptomics. Deep learning models map these different modalities together to correct technical variation and extract unified biological signals.

Do I need PyTorch and Python to run deep learning models for differential expression analysis?▼

Deep learning-based differential expression analysis requires Python, PyTorch, and PyTorch Lightning to run. This Skill is designed for advanced users familiar with these frameworks to perform deep generative modeling on high-throughput omics data.

What is the best way to annotate single-cell types semi-supervised?▼

Semi-supervised annotation of single-cell types is best performed using the scANVI model, an advanced deep learning approach. It extends unsupervised scVI models by leveraging labeled data to accurately classify cell types and correct technical variation.

When should I use deep learning over traditional methods for single-cell omics batch correction?▼

Deep learning batch correction is ideal for high-throughput single-cell omics studies where technical variation obscures biological signals. Models like scVI handle complex batch effects across RNA-seq, ATAC-seq, and spatial transcriptomics better than traditional methods.

How does RNA velocity analysis work with single-cell transcriptomics?▼

RNA velocity analysis for single-cell transcriptomics uses the VeloVI deep learning model to infer the splicing dynamics of cells. This unsupervised approach helps predict the future state of cells based on high-throughput RNA-seq data.