scvi-tools

Topics in the source

• scvi-tools
• Overview
• When to Use This Skill
• Core Capabilities
• 1. Single-Cell RNA-seq Analysis
• 2. Chromatin Accessibility (ATAC-seq)
• 3. Multimodal & Multi-omics Integration
• 4. Spatial Transcriptomics

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. 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 scvi-tools provides models organized by data modality: 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

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