scvi-tools
Deep probabilistic analysis of single-cell omics data.
Decision gist · record as of 2026-08-14
Yes. scvi-tools is actively maintained, has low install friction, carries a permissive license, and is widely used in single-cell genomics research. Install it if you work with single-cell omics data and need probabilistic modeling, integration, or annotation. Ensure PyTorch is installed first and compatible with your hardware.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch installation compatible with your hardware (CPU or GPU); consult PyTorch documentation for GPU-specific setup.
- Low install friction with a pure-wheel distribution.
- Active maintenance with a recent release (36 days ago) and ongoing repository activity.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only copyright and license retention in source distributions.
last release 2026-07-09 (36 days) · last repo commit 2026-08-12 · 1,673 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,631 downloads/mo, #13,069 on PyPI
Alternatives
Verify before relying
pip install scvi-tools
import scvi
import anndata
# Load single-cell data into AnnData object
adata = anndata.read_h5ad('data.h5ad')
# Initialize and train a model (e.g., scVI for dimensionality reduction)
scvi.model.SCVI.setup_anndata(adata)
model = scvi.model.SCVI(adata)
model.train()- Whether GPU acceleration is automatic or requires explicit configuration beyond standard PyTorch setup.
- Performance characteristics and scalability limits for typical single-cell dataset sizes.
- Availability and quality of pre-trained models or transfer learning capabilities.
What it is and what it does
scvi-tools is a Python library for probabilistic modeling and analysis of single-cell omics data. It provides a collection of pre-built models that handle common analysis tasks—dimensionality reduction, data integration across batches, automated cell-type annotation, doublet detection, and spatial deconvolution—all with a unified API that integrates with the Scanpy ecosystem and AnnData data structures. The package is built on PyTorch and PyTorch Lightning, enabling GPU acceleration for large-scale analyses.
Beyond pre-built models, scvi-tools also serves as a framework for developing and deploying novel probabilistic models. It provides building blocks powered by PyTorch Lightning and Pyro, allowing researchers to prototype custom models and integrate them into the same high-level API. The package is part of the scverse ecosystem and is actively maintained by the Yosef Lab at the Weizmann Institute of Science.
Use it for
- Reduce dimensionality of high-dimensional single-cell RNA-seq data for visualization and downstream analysis.
- Integrate multiple single-cell datasets from different batches, technologies, or studies into a unified representation.
- Automatically assign cell types to individual cells based on learned probabilistic representations.
- Identify and remove doublets (cell multiplets) from droplet-based single-cell experiments.
- Deconvolve spatial transcriptomics data to infer cell-type composition and localization.
- Develop and validate new probabilistic models for single-cell analysis using the framework's building blocks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
scvi-tools is actively maintained, has low install friction, carries a permissive license, and is widely used in single-cell genomics research. Install it if you work with single-cell omics data and need probabilistic modeling, integration, or annotation. Ensure PyTorch is installed first and compatible with your hardware.
Install
scvi-tools on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a recent release (36 days ago) and ongoing repository activity. Requires modern Python (3.12+) and a compatible PyTorch installation, particularly for GPU support.
Requires PyTorch installation compatible with your hardware (CPU or GPU); consult PyTorch documentation for GPU-specific setup.
License in practice
BSD 3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only copyright and license retention in source distributions.
Quickstart
pip install scvi-tools
import scvi
import anndata
# Load single-cell data into AnnData object
adata = anndata.read_h5ad('data.h5ad')
# Initialize and train a model (e.g., scVI for dimensionality reduction)
scvi.model.SCVI.setup_anndata(adata)
model = scvi.model.SCVI(adata)
model.train()Verify before relying
- Whether GPU acceleration is automatic or requires explicit configuration beyond standard PyTorch setup.
- Performance characteristics and scalability limits for typical single-cell dataset sizes.
- Availability and quality of pre-trained models or transfer learning capabilities.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 19 packagesanndatadocreplightningml-collectionsmudatanumbanumpypandaspyro-pplrichscanpyscikit-learnscipysparsetensorboardtorchtorchmetricstqdmxarray |
| Maintenance | Actively maintained 36 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,631 / month, #13,069 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Bio-Informatics |
Evidence: scvi_tools-1.5.0.post1-py3-none-any.whl
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