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 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

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