Update README.md

README.md

@@ -10,15 +10,16 @@ short_description: Probabilistic modeling and analysis of single-cell omics dat
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 # **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).
+Welcome to the **scvi-tools** organization. We provide state-of-the-art probabilistic models tailored for analyzing single-cell omics data, enabling researchers to gain meaningful 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. These models are scalable and extensively documented.
+scvi-tools offers a comprehensive suite of models designed to address various challenges in single-cell data analysis.
 
-### **Key Models**
+### **Current HubModels**
 - **[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.
@@ -33,8 +34,8 @@ scvi-tools offers a comprehensive suite of models designed to address various ch
 - **[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.
+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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@@ -42,12 +43,13 @@ Please reach out on [discourse](https://discourse.scverse.org) if you want to ad
 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.
+- Multi-modal integration of single-cell RNA, ATAC 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)**.
+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).
 
-To learn how to 
 
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@@ -65,15 +67,8 @@ To learn how to
 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)
-
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-## **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.
-
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 ## **Contact**