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| 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). | |
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| ## **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. | |
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| ## **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) | |
| <!--- | |
| - **[MultiVI](https://docs.scvi-tools.org/en/stable/user_guide/models/multivi.html)**: | |
| - A multi-modal model for joint analysis of RNA, ATAC and protein data, enabling integrative insights from diverse omics data. | |
| --> |