README.md

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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
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# **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.

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## **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).


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## **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

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## **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)


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- **[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.
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