{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"scvi-tools provides probabilistic models for analyzing single-cell omics data, including dimensionality reduction, data integration, automated annotation, doublet detection, and spatial deconvolution, built on PyTorch and AnnData.","skillfed_tags":["single-cell-genomics","probabilistic-modeling","bioinformatics"],"use_cases":["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."],"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\u2014dimensionality reduction, data integration across batches, automated cell-type annotation, doublet detection, and spatial deconvolution\u2014all 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.\n\nBeyond 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.","worth_installing":"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."},"id":"scvi-tools","links":{"html":"https://skillfed.io/packages/scvi-tools","md":"https://skillfed.io/packages/scvi-tools.md","pypi":"https://pypi.org/project/scvi-tools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-09","license_spdx":null,"license_treatment":"permissive","name":"scvi-tools","python_support":"supports_current","summary":"Deep probabilistic analysis of single-cell omics data."},"popularity":{"monthly_downloads":98631,"position":13069,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0.post1"}