scvi-tools Deep Learning Skill

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

Workflow Reference Files

CLI Scripts

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

Pipeline Scripts

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:

Critical Requirements

1. 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")
   

2. 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()
   

3. 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