--- title: README emoji: 🦀 colorFrom: green colorTo: gray sdk: static pinned: true license: bsd-3-clause short_description: Probabilistic modeling of single-cell omics data --- # **scvi-tools** Welcome to the **scvi-tools** organization. We provide state-of-the-art probabilistic models tailored for analyzing single-cell omics data. Those enable researchers to gain biological insights with scalable algorithms. These models provide a consistent API making it easy to integrate it into your current analysis pipeline. **scvi-tools** is part of [scverse](https://scverse.org). This is an open science initiative, please contribute your own models to allow the single-cell community to leverage your reference datasets. Learn how to upload your model in our [HubModel tutorials](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/hub/scvi_hub_upload_and_large_files.html). --- ## **Model Overview** scvi-tools offers a comprehensive suite of models designed to address various challenges in single-cell data analysis. ### **Current HubModels** - **[scVI](https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html)**: - A variational autoencoder for dimensionality reduction, batch correction, and clustering. - See all models in [scVI collection](https://huggingface.co./collections/scvi-tools/scvi-673c2c0f2bf4163ef14d018d) - **[scANVI](https://docs.scvi-tools.org/en/stable/user_guide/models/scanvi.html)**: - A semi-supervised model designed for label prediction, especially in cases of partially labeled data. - See all models at [scANVI collection](https://huggingface.co./collections/scvi-tools/scanvi-673c3a4aabddf849496e9079) - **[totalVI](https://docs.scvi-tools.org/en/stable/user_guide/models/totalvi.html)**: - A multi-modal model for joint analysis of RNA and protein data, additionally allowing imputation of missing protein data. - See all models in [totalVI collection](https://huggingface.co./collections/scvi-tools/totalvi-673c3d67e2882005a1d180c1) - **[DestVI](https://docs.scvi-tools.org/en/stable/user_guide/models/destvi.html)**: - A deconvolution model for prediction of single-cell profiles given subcellular spatial transcriptomics data. We provide here pre-trained single cell models (CondSCVI). - See all models in [DestVI collection](https://huggingface.co./collections/scvi-tools/destvi-673c3dbf537347953810a215) - **[Stereoscope](https://docs.scvi-tools.org/en/stable/user_guide/models/stereoscope.html)**: - A deconvolution model for prediction of cell-type composition given subcellular spatial transcriptomics data. We provide here pre-trained single cell models. - See all models in [Stereoscope collection](https://huggingface.co./collections/scvi-tools/stereoscope-673c3ddcf1f9f7542b8819d6) Explore the full list of models in scvi-tools in our **[user guide](https://docs.scvi-tools.org/en/stable/user_guide/index.html)**. Please reach out on [discourse](https://discourse.scverse.org), if you want to add additional models to HuggingFace. --- ## **Key Applications** These models have been applied to a wide array of biological questions, such as: - Batch correction across experiments. - Identification of rare cell populations. - Multi-modal integration of single-cell RNA, and protein data. - Differential expression and abundance analysis in disease contexts. For hands-on examples, refer to our **[tutorials](https://docs.scvi-tools.org/en/stable/tutorials/index.html)**. Learn how to apply scvi-hub for analysis of query datasets in our [HLCA tutorial](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/scrna/query_hlca_knn.html). Discover how to efficiently access CELLxGENE census using our minified models in our [CELLxGENE census tutorial](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/hub/cellxgene_census_model.html). --- ## **Publications** - **[Original scvi-tools Paper](https://www.nature.com/articles/s41587-021-01206-w)**: - Published in *Nature Biotechnology*, this paper introduces the foundational principles of scvi-tools. - **[scvi-hub Preprint](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v1)**: - This manuscript showcases real-world applications of scvi-hub in diverse biological contexts and provides building blocks - to apply these models in your own research --- ## **How to Get Started** 1. Visit our **[official documentation](https://docs.scvi-tools.org)** to get started with installation and explore our API. 2. Follow our **[tutorials](https://docs.scvi-tools.org/en/stable/tutorials/index.html)** for step-by-step guides on using scvi-tools effectively. 3. Dive into our **[models](https://docs.scvi-tools.org/en/stable/user_guide/index.html)** to see how to apply them to your single-cell analysis. --- --- ## **Contact** - Website: [https://scvi-tools.org](https://scvi-tools.org) - GitHub: [https://github.com/scverse/scvi-tools](https://github.com/scverse/scvi-tools) - Tutorials: [scvi-tools Tutorials](https://docs.scvi-tools.org/en/stable/tutorials/index.html) - User questions: [Discourse](https://discourse.scverse.org)