scverse/scvi-tools

Health v2 · maintenance only

Adoption not part of the score

1681 stars · 471 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

scvi-tools is a Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data, built on PyTorch, PyTorch Lightning, Pyro, and AnnData. It provides ready-to-use models (scVI, scANVI, totalVI, Stereoscope) for tasks like dimensionality reduction, data integration, and automated annotation, plus building blocks for developing new probabilistic models.

Use cases

• integrate multiple single-cell RNA-seq datasets
• reduce dimensionality of scRNA-seq data
• automatically annotate cell types
• detect doublets in single-cell data
• deconvolve spatial transcriptomics data
• analyze CITE-seq multi-omic data
• perform factor analysis on single-cell omics
• develop custom variational autoencoder models for genomics

When to choose

• analyzing single-cell or spatial omics data with deep generative models
• batch-correcting and integrating heterogeneous scRNA-seq datasets
• building novel probabilistic models on top of a shared PyTorch/AnnData infrastructure
• needing GPU-accelerated, scalable inference for large single-cell datasets

When to avoid

• you need simple exploratory analysis without deep learning overhead
• your data is bulk omics rather than single-cell
• you require a non-Python workflow without any Python integration
• you lack GPU resources and datasets are very large

Facets

library · maturity stable

machine-learning deep-learning data-science llm-training bioinformatics machine-learning data-science python cross-platform single-cell omics variational-inference pytorch anndata scanpy scrna-seq spatial-omics generative-models gpu

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markdown · JSON · MCP: product_card(name="scverse/scvi-tools")