scvi-toolslisted

# 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 | | RN