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