Update README.md

README.md

@@ -10,7 +10,7 @@ short_description: Probabilistic modeling and analysis of single-cell omics dat
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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, enabling researchers to gain meaningful biological insights with scalable algorithms.  
+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).
 
@@ -22,13 +22,14 @@ scvi-tools offers a comprehensive suite of models designed to address various ch
 ### **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.
-  - Ideal for processing single-cell RNA-seq data.
 - **[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.
 - **[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.
+<!---
 - **[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.
+-->
 - **[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).
 - **[Stereoscope](https://docs.scvi-tools.org/en/stable/user_guide/models/stereoscope.html)**:
@@ -43,7 +44,7 @@ Please reach out on [discourse](https://discourse.scverse.org), if you want to a
 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, ATAC and protein data.
+- 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)**.  
@@ -55,7 +56,7 @@ Discover how to efficiently access CELLxGENE census using our minified models in
 
 ## **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.
+  - 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
@@ -65,7 +66,7 @@ Discover how to efficiently access CELLxGENE census using our minified models in
 ## **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.
+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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