scvi-toolslisted

# 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-ce