name: scvi-tools description: Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space. measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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

references/environment_setup.md

references/data_preparation.md

references/scrna_integration.md

references/atac_peakvi.md

references/citeseq_totalvi.md

references/multiome_multivi.md

references/spatial_deconvolution.md

references/label_transfer.md

references/scarches_mapping.md

references/batch_correction_sysvi.md

references/rna_velocity_velovi.md

references/troubleshooting.md

CLI Scripts

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

Pipeline Scripts

prepare_data.py

python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch

train_model.py

python scripts/train_model.py prepared.h5ad results/ --model scvi

cluster_embed.py

python scripts/cluster_embed.py adata.h5ad results/

differential_expression.py

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

transfer_labels.py

python scripts/transfer_labels.py ref_model/ query.h5ad results/

integrate_datasets.py

python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad

validate_adata.py

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

prepare_adata()

train_scvi()

evaluate_integration()

get_marker_genes()

save_results()

auto_select_model()

quick_clustering()

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