---
name: scvi-tools
description: Trains and applies scvi-tools probabilistic deep generative models (scVI, scANVI, totalVI, MultiVI, PeakVI, DestVI, Solo, CellAssign, MrVI and others) on AnnData or MuData single-cell data using PyTorch. Covers batch correction, integration, cell type annotation, probabilistic differential expression, and scRNA-seq, ATAC-seq, CITE-seq, spatial and methylation data. Use when integrating batches or datasets with scVI, annotating cells with scANVI, jointly modeling RNA and protein or ATAC, deconvolving spatial spots, or testing differential expression with uncertainty. For standard preprocessing and clustering pipelines, use scanpy instead.
license: BSD-3-Clause license
metadata:
  version: '1.2'
  category: life-sciences
  maintainer: Kalaris Labs
---

# scvi-tools

## Overview

scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities. Current stable release: **scvi-tools 1.4.3** (May 2026).

**Model namespaces matter:** core models (scVI, scANVI, totalVI, MultiVI, PeakVI, AUTOZI, CondSCVI, DestVI, LinearSCVI, AmortizedLDA, JaxSCVI) live under `scvi.model`. Most other models (VeloVI, contrastiveVI, CellAssign, PoissonVI, scBasset, MrVI, MethylVI/MethylANVI, CytoVI, SysVI, Decipher, gimVI, scVIVA, ResolVI, Stereoscope, Solo, totalANVI, DIAGVI) live under `scvi.external`. The reference files specify the correct namespace per model.

## When to Use This Skill

Use this skill when:
- Analyzing single-cell RNA-seq data (dimensionality reduction, batch correction, integration)
- Working with single-cell ATAC-seq or chromatin accessibility data
- Integrating multimodal data (CITE-seq, multiome, paired/unpaired datasets)
- Analyzing spatial transcriptomics data (deconvolution, spatial mapping)
- Performing differential expression analysis on single-cell data
- Conducting cell type annotation or transfer learning tasks
- Working with specialized single-cell modalities (methylation, cytometry, RNA velocity)
- Building custom probabilistic models for single-cell analysis

## Core Capabilities

scvi-tools provides models organized by data modality:

### 1. Single-Cell RNA-seq Analysis
Core models for expression analysis, batch correction, and integration. See `references/models-scrna-seq.md` for:
- **scVI**: Unsupervised dimensionality reduction and batch correction
- **scANVI**: Semi-supervised cell type annotation and integration
- **AUTOZI**: Zero-inflation detection and modeling
- **VeloVI**: RNA velocity analysis
- **contrastiveVI**: Perturbation effect isolation

### 2. Chromatin Accessibility (ATAC-seq)
Models for analyzing single-cell chromatin data. See `references/models-atac-seq.md` for:
- **PeakVI**: Peak-based ATAC-seq analysis and integration
- **PoissonVI**: Quantitative fragment count modeling
- **scBasset**: Deep learning approach with motif analysis

### 3. Multimodal & Multi-omics Integration
Joint analysis of multiple data types. See `references/models-multimodal.md` for:
- **totalVI**: CITE-seq protein and RNA joint modeling
- **totalANVI**: Semi-supervised CITE-seq (totalVI with cell-type labels)
- **MultiVI**: Paired and unpaired multi-omic integration (MuData-based)
- **MrVI**: Multi-resolution cross-sample analysis
- **DIAGVI**: Diagonal integration of unpaired single-cell datasets (added in 1.4.3)

### 4. Spatial Transcriptomics
Spatially-resolved transcriptomics analysis. See `references/models-spatial.md` for:
- **DestVI**: Multi-resolution spatial deconvolution
- **Stereoscope**: Cell type deconvolution
- **Tangram**: Spatial mapping and integration
- **scVIVA**: Cell-environment relationship analysis

### 5. Specialized Modalities
Additional specialized analysis tools. See `references/models-specialized.md` for:
- **MethylVI/MethylANVI**: Single-cell methylation analysis
- **CytoVI**: Flow/mass cytometry batch correction
- **Solo**: Doublet detection
- **CellAssign**: Marker-based cell type annotation

## Typical Workflow

All scvi-tools models follow a consistent API pattern:

```python
# 1. Load and preprocess data (AnnData format)
import scvi
import scanpy as sc

adata = scvi.data.heart_cell_atlas_subsampled()
sc.pp.filter_genes(adata, min_counts=3)
sc.pp.highly_variable_genes(adata, n_top_genes=1200)

# 2. Register data with model (specify layers, covariates)
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",  # Use raw counts, not log-normalized
    batch_key="batch",
    categorical_covariate_keys=["donor"],
    continuous_covariate_keys=["percent_mito"]
)

# 3. Create and train model
model = scvi.model.SCVI(adata)
model.train()

# 4. Extract latent representations and normalized values
latent = model.get_latent_representation()
normalized = model.get_normalized_expression(library_size=1e4)

# 5. Store in AnnData for downstream analysis
adata.obsm["X_scVI"] = latent
adata.layers["scvi_normalized"] = normalized

# 6. Downstream analysis with scanpy
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata)
```

**Key Design Principles:**
- **Raw counts required**: Models expect unnormalized count data for optimal performance
- **Unified API**: Consistent interface across all models (setup → train → extract)
- **AnnData-centric**: Seamless integration with the scanpy ecosystem
- **GPU acceleration**: Automatic utilization of available GPUs
- **Batch correction**: Handle technical variation through covariate registration

## Common Analysis Tasks

### Differential Expression
Probabilistic DE analysis using the learned generative models:

```python
de_results = model.differential_expression(
    groupby="cell_type",
    group1="TypeA",
    group2="TypeB",
    mode="change",  # Use composite hypothesis testing
    delta=0.25      # Minimum effect size threshold
)
```

See `references/differential-expression.md` for detailed methodology and interpretation.

### Model Persistence
Save and load trained models:

```python
# Save model
model.save("./model_directory", overwrite=True)

# Load model
model = scvi.model.SCVI.load("./model_directory", adata=adata)
```

### Batch Correction and Integration
Integrate datasets across batches or studies:

```python
# Register batch information
scvi.model.SCVI.setup_anndata(adata, batch_key="study")

# Model automatically learns batch-corrected representations
model = scvi.model.SCVI(adata)
model.train()
latent = model.get_latent_representation()  # Batch-corrected
```

## Theoretical Foundations

scvi-tools is built on:
- **Variational inference**: Approximate posterior distributions for scalable Bayesian inference
- **Deep generative models**: VAE architectures that learn complex data distributions
- **Amortized inference**: Shared neural networks for efficient learning across cells
- **Probabilistic modeling**: Principled uncertainty quantification and statistical testing

See `references/theoretical-foundations.md` for detailed background on the mathematical framework.

## Additional Resources

- **Workflows**: `references/workflows.md` contains common workflows, best practices, hyperparameter tuning, and GPU optimization
- **Model References**: Detailed documentation for each model category in the `references/` directory
- **Official Documentation**: https://docs.scvi-tools.org/en/stable/
- **Tutorials**: https://docs.scvi-tools.org/en/stable/tutorials/index.html
- **API Reference**: https://docs.scvi-tools.org/en/stable/api/index.html

## Installation

Requires Python **3.12+** (scvi-tools 1.4 dropped older versions).

```bash
uv pip install scvi-tools
# For GPU support
uv pip install "scvi-tools[cuda]"
```

For reproducible environments, pin a version: `uv pip install scvi-tools==1.4.3`.

**Compute backends:** training defaults to PyTorch (CPU/GPU/TPU). A JAX backend
(`scvi.model.JaxSCVI`) and an experimental MLX backend for Apple silicon
(`scvi.model.mlxSCVI`) are available for select models.

## Best Practices

1. **Use raw counts**: Always provide unnormalized count data to models
2. **Filter genes**: Remove low-count genes before analysis (e.g., `min_counts=3`)
3. **Register covariates**: Include known technical factors (batch, donor, etc.) in `setup_anndata`
4. **Feature selection**: Use highly variable genes for improved performance
5. **Model saving**: Always save trained models to avoid retraining
6. **GPU usage**: Enable GPU acceleration for large datasets (`accelerator="gpu"`)
7. **Scanpy integration**: Store outputs in AnnData objects for downstream analysis

## Agent operating procedure

1. **Check the environment.** Confirm tool versions, the reference genome/annotation build and the input formats (FASTQ, BAM, VCF, h5ad).
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

## Related skills

- `scanpy`: Standard single-cell RNA-seq analysis pipeline.
- `anndata`: Data structure for annotated matrices in single-cell analysis.
- `cellxgene-census`: Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data.