scvi-tools

# 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: ```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 ```bash 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