Scvi Tools Skill for Claude

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

• Identify the appropriate workflow from the model/workflow tables below
• Read the corresponding reference file for detailed steps and code
• Use scripts in scripts/ to avoid rewriting common code
• For installation or GPU issues, consult references/environment_setup.md
• 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 correction |

| ATAC-seq Analysis | references/atac_peakvi.md | PeakVI for accessibility |

| CITE-seq Analysis | references/citeseq_totalvi.md | totalVI for protein+RNA |

| Multiome Analysis | references/multiome_multivi.md | MultiVI for RNA+ATAC |

| Spatial Deconvolution | references/spatial_deconvolution.md | DestVI spatial analysis |

| Label Transfer | references/label_transfer.md | scANVI reference mapping |

| scArches Mapping | references/scarches_mapping.md | Query-to-reference mapping |

| Batch Correction | references/batch_correction_sysvi.md | Advanced batch methods |

| RNA Velocity | references/rna_velocity_velovi.md | veloVI dynamics |

| Troubleshooting | references/troubleshooting.md | Common issues and solutions |

CLI Scripts

Modular scripts for common workflows. Chain together or modify as needed.

Pipeline Scripts

| Script | Purpose | Usage |

|--------|---------|-------|

| prepare_data.py | QC, filter, HVG selection | python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch |

| train_model.py | Train any scvi-tools model | python scripts/train_model.py prepared.h5ad results/ --model scvi |

| cluster_embed.py | Neighbors, UMAP, Leiden | python scripts/cluster_embed.py adata.h5ad results/ |

| differential_expression.py | DE analysis | python scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden |

| transfer_labels.py | Label transfer with scANVI | python scripts/transfer_labels.py ref_model/ query.h5ad results/ |

| integrate_datasets.py | Multi-dataset integration | python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad |

| validate_adata.py | Check data compatibility | python scripts/validate_adata.py data.h5ad --batch-key batch |

Example Workflow

# 1. Validate input data
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest

# 2. Prepare data (QC, HVG selection)
python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000

# 3. Train model
python scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch

# 4. Cluster and visualize
python scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8

# 5. Differential expression
python scripts/differential_expression.py results/model results/adata_clustered.h5ad results/de.csv --groupby leiden

Python Utilities

The scripts/model_utils.py provides importable functions for custom workflows:

| Function | Purpose |

|----------|---------|

| prepare_adata() | Data preparation (QC, HVG, layer setup) |

| train_scvi() | Train scVI or scANVI |

| evaluate_integration() | Compute integration metrics |

| get_marker_genes() | Extract DE markers |

| save_results() | Save model, data, plots |

| auto_select_model() | Suggest best model |

| quick_clustering() | Neighbors + UMAP + Leiden |

Critical Requirements

• Raw counts required: scvi-tools models require integer count data

   adata.layers["counts"] = adata.X.copy()  # Before normalization
   scvi.model.SCVI.setup_anndata(adata, layer="counts")

• HVG selection: Use 2000-4000 highly variable genes

   sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch", layer="counts", flavor="seurat_v3")
   adata = adata[:, adata.var['highly_variable']].copy()

• Batch information: Specify batch_key for integration

   scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")

Quick Decision Tree

Need to integrate scRNA-seq data?
├── Have cell type labels? → scANVI (references/label_transfer.md)
└── No labels? → scVI (references/scrna_integration.md)

Have multi-modal data?
├── CITE-seq (RNA + protein)? → totalVI (references/citeseq_totalvi.md)
├── Multiome (RNA + ATAC)? → MultiVI (references/multiome_multivi.md)
└── scATAC-seq only? → PeakVI (references/atac_peakvi.md)

Have spatial data?
└── Need cell type deconvolution? → DestVI (references/spatial_deconvolution.md)

Have pre-trained reference model?
└── Map query to reference? → scArches (references/scarches_mapping.md)

Need RNA velocity?
└── veloVI (references/rna_velocity_velovi.md)

Strong cross-technology batch effects?
└── sysVI (references/batch_correction_sysvi.md)

Key Resources

• scvi-tools Documentation
• scvi-tools Tutorials
• Model Hub
• GitHub Issues