scvi-tools

SKILL.md

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