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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.

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
• MultiVI : Paired and unpaired multi-omic integration
• MrVI : Multi-resolution cross-sample analysis

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

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

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

Weekly Installs

145

Repository

davila7/claude-…emplates

GitHub Stars

23.4K

First Seen

Jan 21, 2026

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