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metadata
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.

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:
    • A variational autoencoder for dimensionality reduction, batch correction, and clustering.
    • Ideal for processing single-cell RNA-seq data.
  • SCANVI:
    • A semi-supervised model designed for label prediction, especially in cases of partially labeled data.
  • TOTALVI:
    • A multi-modal model for joint analysis of RNA and protein data, additionally allowing imputation of missing protein data.
  • MultiVI:
    • A multi-modal model for joint analysis of RNA, ATAC and protein data, enabling integrative insights from diverse omics data.
  • DestVI:
    • A deconvolution model for prediction of single-cell profiles given subcellular spatial transcriptomics data. We provide here pre-trained single cell models.
  • 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. Please reach out on discourse 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.

To learn how to

Publications

  • Original scvi-tools Paper:
    • Published in Nature Biotechnology, this paper introduces the foundational principles and applications 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

  1. Visit our official documentation to get started with installation and explore our API.
  2. Follow our tutorials for step-by-step guides on using scvi-tools effectively.
  3. Dive into our models 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 Discover how to efficiently access CELLxGENE census using our minified models in our CELLxGENE census tutorial

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