scvi-tools

Analyze single-cell RNA sequencing data with scVI and scANVI models.

Updated Feb 6, 2026

Search Tags:#genomics #single-cell #batch correction #data integration #scRNA-seq #scVI #scANVI

Ready to run

npx skills add https://github.com/lohasle/knowledge-work-plugins --skill scvi-tools-lohasle

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/lohasle/knowledge-work-plugins/tree/main/bio-research/skills/scvi-tools
Command: npx skills add https://github.com/lohasle/knowledge-work-plugins --skill scvi-tools-lohasle

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, scvi-tools, anndata, torch, mudata, scvelo, squidpy, pdf2image, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies complex single-cell genomics analysis, enabling researchers to integrate datasets, correct batch effects, and identify cell types with advanced deep learning models.

Core Features & Use Cases

Data Integration: Seamlessly combine datasets from different experiments or technologies.
Batch Correction: Remove technical variations to reveal true biological signals.
Cell Type Annotation: Leverage pre-trained models or transfer labels for accurate cell identification.
Use Case: You have scRNA-seq data from multiple donors processed on different days. Use this Skill to integrate them, correct for batch effects, and then identify distinct cell populations like T cells, B cells, and macrophages.

Quick Start

Use the scvi-tools skill to integrate my single-cell RNA sequencing data from different batches.

Frequently Asked Questions about scvi-tools

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

FAQPage Schema

How do I integrate scRNA-seq data from different batches?▼

To integrate scRNA-seq data from different batches, you can use deep generative models like scVI to remove technical variations and combine datasets. This process corrects batch effects to reveal true biological signals across multiple experiments.

How does batch correction work for single-cell RNA sequencing data?▼

Batch correction for single-cell RNA sequencing data works by applying deep generative models like scVI to remove technical variations. This process isolates true biological signals by conditioning on batch information provided in the AnnData object.

What do I need to prepare before running scVI for single-cell data integration?▼

Before running scVI for single-cell data integration, you need to prepare AnnData objects containing raw counts and optional batch or label information. This structured data format is required to train the deep generative models effectively.

Can I transfer cell type labels across multiple scRNA-seq datasets?▼

Yes, you can transfer cell type labels across multiple scRNA-seq datasets using the scANVI model. It leverages deep learning to accurately identify and annotate cell populations by transferring labels from a reference dataset.

What's the best way to identify distinct cell populations like T cells and B cells?▼

The best way to identify distinct cell populations like T cells and B cells is using scANVI for cell type annotation. It leverages deep generative models to accurately transfer labels and identify specific cell types within integrated scRNA-seq data.

Why does my scVI model require raw counts in the AnnData object?▼

Your scVI model requires raw counts in the AnnData object because the deep generative model architecture is specifically designed to model unnormalized scRNA-seq data. Using normalized data would disrupt the underlying statistical assumptions needed for batch correction.