scvi-tools

# scvi-tools Deep Learning Skill

This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.

## How to Use This Skill

1. Identify the appropriate workflow from the model/workflow tables below
2. Read the corresponding reference file for detailed steps and code
3. Use scripts in `scripts/` to avoid rewriting common code
4. For installation or GPU issues, consult `references/environment_setup.md`
5. For debugging, consult `references/troubleshooting.md`

## When to Use This Skill

- When scvi-tools, scVI, scANVI, or related models are mentioned
- When deep learning-based batch correction or integration is needed
- When working with multi-modal data (CITE-seq, multiome)
- When reference mapping or label transfer is required
- When analyzing ATAC-seq or spatial transcriptomics data
- When learning latent representations of single-cell data

## Model Selection Guide

| Data Type | Model | Primary Use Case |
|-----------|-------|------------------|
| scRNA-seq | **scVI** | Unsupervised integration, DE, imputation |
| scRNA-seq + labels | **scANVI** | Label transfer, semi-supervised integration |
| CITE-seq (RNA+protein) | **totalVI** | Multi-modal integration, protein denoising |
| scATAC-seq | **PeakVI** | Chromatin accessibility analysis |
| Multiome (RNA+ATAC) | **MultiVI** | Joint modality analysis |
| Spatial + scRNA reference | **DestVI** | Cell type deconvolution |
| RNA velocity | **veloVI** | Transcriptional dynamics |
| Cross-technology | **sysVI** | System-level batch correction |

## Workflow Reference Files

| Workflow | Reference File | Description |
|----------|---------------|-------------|
| Environment Setup | `references/environment_setup.md` | Installation, GPU, version info |
| Data Preparation | `references/data_preparation.md` | Formatting data for any model |
| scRNA Integration | `references/scrna_integration.md` | scVI/scANVI batch correctio