Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For
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Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For
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scvi-tools.SKILL.md
---name: scvi-tools
description: Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
license: BSD-3-Clause license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# 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)
