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scvi-tools

Automate single-cell RNA-seq analysis with scvi-tools deep generative models.

Updated May 8, 2026
Search Tags:#deep learning#single-cell analysis#batch correction#scRNA-seq#cell type annotation
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scvi-tools-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/single-cell-sequencing/scvi-tools
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill scvi-tools-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvi-tools, scanpy, pytorch, pytorch-lightning, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies complex single-cell analysis by integrating deep generative models like scVI and scANVI, enabling users to handle batch effects, perform dimensionality reduction, and annotate cell types without extensive computational expertise.

Core Features & Use Cases

  • scVI: Unsupervised dimensionality reduction and batch correction for scRNA-seq data.
  • scANVI: Semi-supervised cell type annotation and integration.
  • Use Case: Analyze a scRNA-seq dataset to reduce dimensions, perform batch correction, and annotate cell types based on markers provided in the data.

Quick Start

Run the following command to train an scVI model on your scRNA-seq dataset:

scvi-tools train --data 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 scRNA-seq data using deep learning?▼

Batch correction for scRNA-seq data is achieved using the unsupervised scVI model to generate a corrected latent space. This removes technical batch effects while preserving biological variance for accurate downstream analysis.

Can I annotate cell types in scRNA-seq data with deep generative models?▼

Cell type annotation in scRNA-seq data is performed using the semi-supervised scANVI model. It leverages known marker labels alongside variational inference to predict and annotate unknown cell populations within the dataset.

What is the best way to integrate scRNA-seq and ATAC-seq data for dimensionality reduction?▼

Dimensionality reduction for scRNA-seq, ATAC-seq, and spatial transcriptomics is handled by deep generative models. This approach reduces data dimensions while integrating complex multi-modal biological workflows efficiently.

Do I need PyTorch to run scvi-tools for single-cell analysis?▼

PyTorch and PyTorch Lightning are required dependencies for running deep generative models in single-cell analysis. You also need the scanpy library and a basic understanding of single-cell data processing.

How do I train an scVI model on an h5ad dataset?▼

To train an scVI model on your scRNA-seq dataset, execute the command `scvi-tools train --data data.h5ad`. This automates the variational inference process for dimensionality reduction and batch correction.

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