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-title: README
-emoji: 🦀
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-sdk: static
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-license: bsd-3-clause
-short_description: Probabilistic modeling and analysis of single-cell omics dat
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-[scvi-tools](https://github.com/scverse/scvi-tools) (single-cell variational inference tools) is a package for probabilistic modeling and analysis of single-cell omics data, built on top of PyTorch, Jax, and AnnData.
+---
+title: README
+emoji: 🦀
+colorFrom: green
+colorTo: gray
+sdk: static
+pinned: false
+license: bsd-3-clause
+short_description: Probabilistic modeling and analysis of single-cell omics dat
+---
+# **scvi-tools**
+
+Welcome to the **scvi-tools** Model Card. This repository contains state-of-the-art probabilistic models tailored for analyzing single-cell omics data, enabling researchers to gain meaningful biological insights through cutting-edge machine learning techniques.
+**scvi-tools** is a member of the [scverse ecosystem](https://scverse.org).
+
+---
+
+## **Model Overview**
+scvi-tools offers a comprehensive suite of models designed to address various challenges in single-cell data analysis. These models are scalable and extensively documented.
+
+### **Key Models**
+- **[scVI](https://docs.scvi-tools.org/en/stable/user_guide/models.html#scvi)**:
+  - A variational autoencoder for dimensionality reduction, batch correction, and clustering.
+  - Ideal for processing single-cell RNA-seq data.
+- **[SCANVI](https://docs.scvi-tools.org/en/stable/user_guide/models.html#scanvi)**:
+  - A semi-supervised model designed for label prediction, especially in cases of partially labeled data.
+- **[TOTALVI](https://docs.scvi-tools.org/en/stable/user_guide/models.html#totalvi)**:
+  - A multi-modal model for joint analysis of RNA and protein data, additionally allowing imputation of missing protein data.
+- **[MultiVI](https://docs.scvi-tools.org/en/stable/user_guide/models.html#multivi)**:
+  - A multi-modal model for joint analysis of RNA, ATAC and protein data, enabling integrative insights from diverse omics data.
+- **[DestVI](https://docs.scvi-tools.org/en/stable/user_guide/models.html#destvi)**:
+  - A deconvolution model for prediction of single-cell profiles given subcellular spatial transcriptomics data. We provide here pre-trained single cell models.
+- **[Stereoscope](https://docs.scvi-tools.org/en/stable/user_guide/models.html#stereoscope)**:
+  - A deconvolution model for prediction of cell-type composition given subcellular spatial transcriptomics data. We provide here pre-trained single cell models.
+
+Explore the full list of models in our **[documentation](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 scvi-hub.
+
+---
+
+## **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 analysis in disease contexts.
+
+For hands-on examples, refer to our **[tutorials](https://docs.scvi-tools.org/en/stable/tutorials/index.html)**.
+
+To learn how to 
+
+---
+
+## **Publications**
+- **[Original scvi-tools Paper](https://www.nature.com/articles/s41587-021-01206-w)**:
+  - Published in *Nature Biotechnology*, this paper introduces the foundational principles and applications 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 they can transform your single-cell analysis.
+
+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)
+
+---
+
+## **Contributing**
+scvi-tools is an open-source initiative. Contributions are welcome! Join us on GitHub to submit issues, suggest features, or collaborate.
+Contribute your own models to allow the single-cell community to leverage your reference datasets.
+
+---
+
+## **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)