scvi-tools

name	scvi-tools
description	Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
license	BSD-3-Clause license
metadata	{"version":"1.1","skill-author":"K-Dense Inc."}

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:

# 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:

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:

# 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:

# 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).

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