# Scvi Tools

Deep generative models for single-cell omics.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: BSD-3-Clause license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill scvi-tools -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill scvi-tools -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill scvi-tools -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill scvi-tools -g -a gemini-cli -y`

## Description

Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# scvi-tools

## Overview

scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.

## When to Use This Skill

Use this skill when:
- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
- Working with single-cell ATAC-seq or chromatin accessibility data
- Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
- Performing differential expression analysis on single-cell data
- Conducting cell type annotation or transfer learning tasks
- Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)
- Building custom probabilistic models for single-cell analysis

## Core Capabilities

scvi-tools provides models organized by data modality:

### 1. Single-Cell RNA-seq Analysis
Core models for expression analysis, batch correction, and integration. See `references/models-scrna-seq.md` for:
- **scVI**: Unsupervised dimensionality reduction and batch correction
- **scANVI**: Semi-supervised cell type annotation and integration
- **AUTOZI**: Zero-inflation detection and modeling
- **VeloVI**: RNA velocity analysis
- **contrastiveVI**: Perturbation effect isolation

### 2. Chromatin Accessibility (ATAC-seq)
Models for analyzing single-cell chromatin data. See `references/models-atac-seq.md` for:
- **PeakVI**: Peak-based ATAC-seq analysis and integration
- **PoissonVI**: Quantitative fragment count modeling
- **scBasset**: Deep learning approach with motif analysis

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/05-生物信息与基因组学/scvi-tools/SKILL.md