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scvi-tools

Automate single-cell RNA-seq batch integration, cell embedding, and differential expression with scVI and scANVI.

288|34|Updated Jul 6, 2026
Search Tags:#single-cell RNA-seq#differential expression#scVI#gene expression#scANVI#scRNA-seq analysis
One-click install
npx skills add https://github.com/PKU-YuanGroup/OpenAI4S --skill scvi-tools-pku-yuangroup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scvi-tools
Source: https://github.com/PKU-YuanGroup/OpenAI4S/tree/main/skills/scvi-tools
Command: npx skills add https://github.com/PKU-YuanGroup/OpenAI4S --skill scvi-tools-pku-yuangroup

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexities of single-cell RNA-seq data analysis, allowing users to integrate batches, perform cell embedding for clustering, and assess differentially expressed genes per cluster.

Core Features & Use Cases

  • scVI / scANVI: Utilizes deep generative models for unsupervised batch-corrected latent embeddings and semi-supervised cell-type label transfer.
  • Batch Integration: Seamlessly integrates scRNA-seq batches for uniform analysis.
  • Cell Embedding: Embeds cells for clustering, providing a foundation for downstream analysis.
  • DE Scoring: Scores differentially expressed genes per cluster to gain insights into biological processes.
  • Use Case: A researcher can use this Skill to analyze scRNA-seq data, identify cell types, and understand gene expression changes in a biological context.

Quick Start

Train scVI on your single-cell RNA-seq data with the command 'scvi.model.SCVI(adata, n_latent=30).train(max_epochs=200, early_stopping=True, accelerator="gpu", devices=1)'.

Frequently Asked Questions about scvi-tools

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

FAQPage Schema
How do I integrate multiple scRNA-seq batches for uniform clustering?▼

To integrate scRNA-seq batches, you can use scVI to train deep generative models that produce unsupervised, batch-corrected latent cell embeddings for downstream clustering.

What is the best way to score differentially expressed genes in single-cell RNA-seq data?▼

Scoring differentially expressed genes per cluster is achieved by using scVI tools to assess gene expression changes, providing insights into underlying biological processes.

Can I use scANVI for semi-supervised cell-type label transfer on scRNA-seq data?▼

Yes, scANVI enables semi-supervised cell-type label transfer by extending scVI's deep generative models to incorporate known labels during the batch-corrected embedding process.

Do I need a GPU to train scVI models for single-cell RNA-seq analysis?▼

Training scVI models supports GPU acceleration to handle high-throughput biological research, utilizing deep learning capabilities for efficient processing of single-cell RNA-seq data.

How do I train scVI on my scRNA-seq data to embed cells?▼

To embed cells, initialize scvi.model.SCVI on your annotated data and train it using parameters like n_latent and max_epochs with early stopping enabled.

When do I need deep generative models for scRNA-seq analysis?▼

Deep generative models are needed for scRNA-seq analysis when you must simultaneously correct batch effects, generate cell embeddings for clustering, and perform differential expression analysis.

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