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.
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.
Model Overview
scvi-tools offers a comprehensive suite of models designed to address various challenges in single-cell data analysis.
Current HubModels
- scVI:
- A variational autoencoder for dimensionality reduction, batch correction, and clustering.
- See all models in scVI collection
- scANVI:
- A semi-supervised model designed for label prediction, especially in cases of partially labeled data.
- See all models at scANVI collection
- totalVI:
- 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
- DestVI:
- 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
- Stereoscope:
- 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
Explore the full list of models in scvi-tools in our user guide.
Please reach out on discourse, 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.
Learn how to apply scvi-hub for analysis of query datasets in our HLCA tutorial.
Discover how to efficiently access CELLxGENE census using our minified models in our CELLxGENE census tutorial.
Publications
- Original scvi-tools Paper:
- Published in Nature Biotechnology, this paper introduces the foundational principles of scvi-tools.
- scvi-hub Preprint:
- 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
- Visit our official documentation to get started with installation and explore our API.
- Follow our tutorials for step-by-step guides on using scvi-tools effectively.
- Dive into our models to see how to apply them to your single-cell analysis.
Contact